Encoded signal systems and methods to ensure minimal robustness

ABSTRACT

This disclosure relates to advanced signal processing technology including signal encoding and image processing. One implementation describes an encoding system including a masking module. The masking module scales or eliminates signal encoding adjustments based on an image&#39;s luminance or chrominance values. Of course, other implementations, combinations and claims are also provided.

RELATED APPLICATION DATA

This application claims the benefit of U.S. Provisional Patent Application No. 62/521,272, filed Jun. 16, 2017, which is hereby incorporated herein by reference in its entirety, including all drawings and any appendices. This document includes an Appendix A, which corresponds to U.S. Provisional Patent Application No. 62/682,750, filed Jun. 8, 2018, which is hereby incorporated herein by reference in its entirety, including all drawings and any appendices. This application is also related to U.S. Pat. Nos. 9,690,967, 9,635,378, 9,565,335, 9,521,291, 9,380,186, US Published Patent Application No. US 2017-0024840 A1, and related to U.S. patent application Ser. No. 15/918,924, filed Mar. 12, 2018 (published as U.S. 2018-035211 A1, and U.S. Provisional Patent Application No. 62/628,510, filed Feb. 9, 2018, which are each hereby incorporated herein by reference in its entirety, including all drawings and any appendices.

TECHNICAL FIELD

This disclosure relates to advanced signal processing technology including signal encoding and image processing.

BACKGROUND AND SUMMARY

Portions of this disclosure are described in terms of, e.g., encoded signals for digital designs, product packaging (sometimes just referred to herein as “packaging” or “package”) and other objects, e.g., labels, product hang tags, logos, etc. These encoding techniques can be used, e.g., to alter or transform how color inks are printed on various physical substrates. The alterations or transformations preferably result in a printed design carrying machine readable indicia on a surface of a physical object.

Various forms of signal encoding (or “embedding”) include, e.g., “steganographic encoding” and “digital watermarking.” Digital watermarking is a process for transforming physical or electronic media to embed a machine-readable code (or “auxiliary data”) into the media. In some cases the media is modified such that the embedded code is obscured, yet may be detected through an automated detection process. Digital watermarking is often applied to electronic or physical objects such as printed objects, images, audio signals, and video signals. However, it may also be applied to other types of objects, including, e.g., product packaging, electronics such as circuit boards and CPUs, stickers, logos, product hang tags, line-art, software, multi-dimensional graphics models, and surface textures of such objects.

In this document we use the terms “digital watermark” and “watermark” (and various forms thereof) interchangeably.

Auxiliary data embedding systems typically include two components: an encoder (or embedder) that embeds the auxiliary signal in a host image or object, and a decoder (or detector) that detects and reads the embedded auxiliary signal from the host image or object. The encoder may embed the auxiliary signal by altering or transforming a host image or object to carry the auxiliary data. The detection component analyzes a suspect image, object or signal to detect whether an auxiliary signal is present, and if so, extracts or reads information carried in it.

Several particular digital watermarking and auxiliary data embedding techniques have been developed. The reader is presumed to be familiar with the literature in this field. Particular techniques for embedding and detecting imperceptible digital watermarks are detailed in the assignee's patent documents including US Published Patent Application No. 20150156369; U.S. patent application Ser. No. 14/725,399, filed May 29, 2015 (now U.S. Pat. No. 9,635,378), Ser. No. 14/724,729, filed May 28, 2015 (published as US 2016-0217547 A1), and Ser. No. 14/842,575, filed Sep. 1, 2015 (published as US 2017-0004597 A1); International Application No. PCT/US2015/44904, filed Aug. 12, 2015 (published as WO 2016025631 A1) and U.S. Pat. Nos. 7,054,461, 7,286,685, and 9,129,277. Related technology is detailed in Assignee's U.S. patent application Ser. No. 15/073,483, filed Mar. 17, 2016 (published as US 2016-0275326 A1). Each of the patent documents mentioned in this paragraph are hereby incorporated herein by reference in its entirety, including all drawings and any appendices.

Further aspects, features and advantages will become even more apparent with reference to the following detailed description, claims and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1a and 1b illustrate example watermarked digital images, which are part of a test set.

FIGS. 2a-2e are graphs showing standard deviation for various strength measures.

FIG. 3 is a diagram showing construction of a message signature.

FIG. 4 is a histogram showing a detectability measure (linear reference pattern strength) in terms of detection stage passes.

FIG. 5 is a histogram showing a detectability measure (message strength) in terms of successful watermark message reads.

FIGS. 6a-6d are diagrams showing relationships between detected and predicted detectability measures.

FIG. 7 is a heat map showing predicted watermark detection per area for an embedded digital image.

FIG. 8 is a diagram of an electronic device (e.g., a smartphone, mobile device, tablet, laptop or other electronic device).

FIG. 9A shows a watermarked image.

FIG. 9B show a corresponding robustness map of the watermarked image in FIG. 9A.

FIG. 10 is a greyscale version of the watermark image (FIG. 9A) with 50% opacity overlaid onto a white background.

FIG. 11 is the FIG. 9A watermarked image masked using the robustness map in FIG. 9B.

FIG. 12 shows a robustness image where color indicates higher probability of digital watermarking being read and grey indicates a lower probability of the digital watermarking being read.

FIG. 13 shows the robustness image of FIG. 12 including horizontal and vertical swipe probabilities.

FIG. 14 is a block diagram of a signal encoder for encoding a digital payload signal into an image signal.

FIG. 15 is a block diagram of a compatible signal decoder for extracting the digital payload signal from an image signal.

FIG. 16 is a flow diagram illustrating operations of a signal generator.

FIG. 17 is a diagram illustrating embedding of an auxiliary signal into host image signal.

FIG. 18 is a flow diagram illustrating a method for decoding a payload signal from a host image signal.

FIG. 19A shows an image segmented into blocks for decoder analysis.

FIG. 19B shows the FIG. 19A image including a smaller blocked area for decoder analysis.

FIG. 19C represents the corresponding blocked area from FIG. 19B, including decoded signals per sub-area.

FIG. 19D represents another instance of the corresponding blocked area from FIG. 19B, including decoded signals per sub-area.

FIG. 19E represents the FIG. 19D instance, relative to a 1D barcode comparison.

FIG. 20A shows an image including an area for analysis.

FIG. 20B represents the corresponding area from FIG. 20A, including various different decoded signals in some of the sub-areas.

FIGS. 21A and 21B are block diagrams for code conflict detectors.

FIG. 22 is a block diagram showing a signal encoding system, including system flow.

FIG. 23 is a diagram showing additional details of ink blending module adapted for signal encoding.

FIG. 24 is a block diagram for a color visibility model.

FIG. 25 is a block diagram for a color visibility model with CSFs varied according to image local luminance.

FIG. 26 is a block diagram for a color visibility model to achieve equal visibility embedding (EVE).

FIG. 27 is a block diagram an embedding embedding system.

FIG. 28 is a graph showing luminance percentage ranges for the Smart Mask module shown in FIG. 27.

FIG. 29 is a state diagram for the Smart Mask module in FIG. 27.

FIG. 30 is a graph showing min dot percentage ranges for the Min Dot module shown in FIG. 27.

DETAILED DESCRIPTION Introduction

The following detailed description is divided into six (6) general sections. It should be understood from the outset, however, that we expressly contemplate combining subject matter from one such section with one or more of the other sections. Thus, the sections and section headings are provided for the reader's convenience and are not intended to impose restrictions or limitations. The sections include: I. Signal Encoder and Decoder; II. Robustness Masks (maps) and Swipe Metrics; III. Construction of Visual Maps Using Robustness as a Mask; IV. Detecting multiple different codes within imagery; V. Ensure Minimal Robustness for Encoded Signals; and VI. Operating Environments.

I. Signal Encoder and Decoder

Encoder/Decoder

FIG. 14 is a block diagram of a signal encoder for encoding a digital payload signal into an image signal. FIG. 15 is a block diagram of a compatible signal decoder for extracting the digital payload signal from an image signal.

While the signal encoder and decoder may be used for communicating a data channel for many applications, one objective for use in physical objects is robust signal communication through images formed on and captured from these objects. Signal encoders and decoders, like those in the Digimarc Barcode Platform from Digimarc Corporation, communicate auxiliary data in a data carrier within image content. Encoding and decoding is applied digitally, yet the signal survives digital to analog transformation and analog to digital transformation. For example, the encoder generates a modulated digital image that is converted to a rendered form, such as a printed image. The modulated digital image includes the encoded signal prior to rendering. Prior to decoding, a receiving device has or communicates with an imager to capture the modulated signal, convert it to an electric signal, which is digitized and then processed by the FIG. 15 signal decoder.

Inputs to the signal encoder include a host image 220 and auxiliary data payload 222. The objectives of the encoder include encoding a robust signal with desired payload capacity per unit of host signal (e.g., a unit may include the spatial area of a two-dimensional tile within the host signal), while maintaining perceptual quality. In some cases, there may be very little variability or presence of a host signal. In this case, there is little host interference on the one hand, yet little host content in which to mask the presence of the data channel within an image. Some examples include a package design that is devoid of much image variability (e.g., a single, uniform color). See, e.g., U.S. Pat. No. 9,635,378, incorporated herein by reference in its entirety.

The auxiliary data payload 222 includes the variable data information to be conveyed in the data channel, possibly along with other protocol data used to facilitate the communication. The protocol of the auxiliary data encoding scheme comprises the format of the auxiliary data payload, error correction coding schemes, payload modulation methods (such as the carrier signal, spreading sequence, encoded payload scrambling or encryption key), signal structure (including mapping of modulated signal to embedding locations within a tile), error detection in payload (CRC, checksum, etc.), perceptual masking method, host signal insertion function (e.g., how auxiliary data signal is embedded in or otherwise combined with host image signal in a package or label design), and/or synchronization method and signals.

The protocol defines the manner in which the signal is structured and encoded for robustness, perceptual quality and/or data capacity. For a particular application, there may be a single protocol, or more than one protocol, depending on application requirements. Examples of multiple protocols include cases where there are different versions of the channel, different channel types (e.g., several digital watermark layers within a host). Different versions may employ different robustness encoding techniques or different data capacity. Protocol selector module 224 determines the protocol to be used by the encoder for generating a data signal. It may be programmed to employ a particular protocol depending on the input variables, such as user control, application specific parameters, or derivation based on analysis of the host signal.

Perceptual analyzer module 226 analyzes the input host signal to determine parameters for controlling signal generation and embedding, as appropriate. It is not necessary in certain applications, while in others it may be used to select a protocol and/or modify signal generation and embedding operations. For example, when encoding in host color images that will be printed or displayed, the perceptual analyzer 256 is used to ascertain color content and masking capability of the host image. The output of this analysis, along with the rendering method (display or printing device) and rendered output form (e.g., ink and substrate) is used to control auxiliary signal encoding in particular color channels (e.g., one or more channels of process inks, Cyan, Magenta, Yellow, or Black (CMYK) or spot colors), perceptual models, and signal protocols to be used with those channels. Please see, e.g., our work on visibility and color models used in perceptual analysis in our U.S. application Ser. No. 14/616,686 (published as US 2015-0156369 A1, issued as U.S. Pat. No. 9,380,186), Ser. No. 14/588,636 (published as US 2015-0187039 A1, and issued as U.S. Pat. No. 9,401,001) and Ser. No. 13/975,919 (now U.S. Pat. No. 9,449,357), Patent Application Publication No. US 2010-0150434 A1 (issued as U.S. Pat. No. 9,117,268), and U.S. Pat. No. 7,352,878, which are hereby incorporated by reference in their entirety.

The perceptual analyzer module 226 also computes a perceptual model, as appropriate, to be used in controlling the modulation of a data signal onto a data channel within image content as described below.

The signal generator module 228 operates on the auxiliary data and generates a data signal according to the protocol. It may also employ information derived from the host signal, such as that provided by perceptual analyzer module 226, to generate the signal. For example, the selection of data code signal and pattern, the modulation function, and the amount of signal to apply at a given embedding location may be adapted depending on the perceptual analysis, and in particular on the perceptual model and perceptual mask that it generates. Please see below and the incorporated patent documents for additional aspects of this process.

Embedder module 230 takes the data signal and modulates it into an image by combining it with the host image. The operation of combining may be an entirely digital signal processing operation, such as where the data signal modulates the host signal digitally, may be a mixed digital and analog process or may be purely an analog process (e.g., where rendered output images, with some signals being modulated data and others being host image content, such as the various layers of a package design file).

There are a variety of different functions for combining the data and host in digital operations. One approach is to adjust the host signal value as a function of the corresponding data signal value at an embedding location, which is limited or controlled according to the perceptual model and a robustness model for that embedding location. The adjustment may be altering the host image by adding a scaled data signal or multiplying by a scale factor dictated by the data signal value corresponding to the embedding location, with weights or thresholds set on the amount of the adjustment according to the perceptual model, robustness model, and/or available dynamic range. The adjustment may also be altering by setting the modulated host signal to a particular level (e.g., quantization level) or moving it within a range or bin of allowable values that satisfy a perceptual quality or robustness constraint for the encoded data. As detailed further below, the signal generator 228 produces a data signal with data elements that are mapped to embedding locations in an image tile. These data elements are modulated onto the host image at the embedding locations. A tile may include a pattern of embedding locations. The tile derives its name from the way in which it is repeated in contiguous blocks of a host signal, but it need not be arranged this way. In image-based encoders, we may use tiles in the form of a two dimensional array (e.g., 128×128, 256×256, 512×512) of embedding locations. The embedding locations correspond to host signal samples at which an encoded signal element is embedded in an embedding domain, such as a spatial domain (e.g., pixels at a spatial resolution), frequency domain (frequency components at a frequency resolution), or some other feature space. We sometimes refer to an embedding location as a bit cell, referring to a unit of data (e.g., an encoded bit or chip element) encoded within a host signal at the location of the cell. Again please see the documents incorporated herein for more information on variations for particular type of media.

The operation of combining may include one or more iterations of adjustments to optimize the modulated host for perceptual quality or robustness constraints. One approach, for example, is to modulate the host image so that it satisfies a perceptual quality metric as determined by perceptual model (e.g., visibility model) for embedding locations across the signal. Another approach is to modulate the host image so that it satisfies a robustness metric across the signal. Yet another is to modulate the host image according to both the robustness metric and perceptual quality metric derived for each embedding location. The incorporated documents provide examples of these techniques. Below, we highlight a few examples. See, e.g., U.S. patent application Ser. No. 13/975,919; and see also, U.S. patent application Ser. No. 14/588,636, filed Jan. 2, 2015 (published as US 2015-0187039 A1), filed Jan. 2, 2015, and Ser. No. 15/137,401, filed Apr. 25, 2016 (now U.S. Pat. No. 9,565,335), which are each hereby incorporated by reference in its entirety.

For color images, the perceptual analyzer generates a perceptual model that evaluates visibility of an adjustment to the host by the embedder and sets levels of controls to govern the adjustment (e.g., levels of adjustment per color direction, and per masking region). This may include evaluating the visibility of adjustments of the color at an embedding location (e.g., units of noticeable perceptual difference in color direction in terms of CIE Lab values), Contrast Sensitivity Function (CSF), spatial masking model (e.g., using techniques described by Watson in US Published Patent Application No. US 2006-0165311 A1, which is incorporated by reference herein in its entirety), etc. One way to approach the constraints per embedding location is to combine the data with the host at embedding locations and then analyze the difference between the encoded host with the original. The perceptual model then specifies whether an adjustment is noticeable based on the difference between a visibility threshold function computed for an embedding location and the change due to embedding at that location. The embedder then can change or limit the amount of adjustment per embedding location to satisfy the visibility threshold function. Of course, there are various ways to compute adjustments that satisfy a visibility threshold, with different sequence of operations. See, e.g., our U.S. patent application Ser. Nos. 14/616,686, 14/588,636 and 13/975,919, Patent Application Publication No. US 2010-0150434 A1, and U.S. Pat. No. 7,352,878, already incorporated herein.

The Embedder also computes a robustness model. The computing of a robustness model may include computing a detection metric for an embedding location or region of locations. The approach is to model how well the decoder will be able to recover the data signal at the location or region. This may include applying one or more decode operations and measurements of the decoded signal to determine how strong or reliable the extracted signal. Reliability and strength may be measured by comparing the extracted signal with the known data signal. Below, we detail several decode operations that are candidates for detection metrics within the embedder. One example is an extraction filter which exploits a differential relationship to recover the data signal in the presence of noise and host signal interference. At this stage of encoding, the host interference is derivable by applying an extraction filter to the modulated host. The extraction filter models data signal extraction from the modulated host and assesses whether the differential relationship needed to extract the data signal reliably is maintained. If not, the modulation of the host is adjusted so that it is.

Detection metrics may be evaluated such as by measuring signal strength as a measure of correlation between the modulated host and variable or fixed data components in regions of the host, or measuring strength as a measure of correlation between output of an extraction filter and variable or fixed data components. Depending on the strength measure at a location or region, the embedder changes the amount and location of host signal alteration to improve the correlation measure. These changes may be particularly tailored so as to establish relationships of the data signal within a particular tile, region in a tile or bit cell pattern of the modulated host. To do so, the embedder adjusts bit cells that violate the relationship so that the relationship needed to encode a bit (or M-ary symbol) value is satisfied and the thresholds for perceptibility are satisfied. Where robustness constraints are dominant, the embedder will exceed the perceptibility threshold where necessary to satisfy a desired robustness threshold.

The robustness model may also model distortion expected to be incurred by the modulated host, apply the distortion to the modulated host, and repeat the above process of measuring detection metrics and adjusting the amount of alterations so that the data signal will withstand the distortion. See, e.g., Ser. Nos. 14/616,686, 14/588,636 and 13/975,919 for image related processing.

This modulated host is then output as an output image signal 232, with a data channel encoded in it. The operation of combining also may occur in the analog realm where the data signal is transformed to a rendered form, such as a layer of ink or coating applied by a commercial press to substrate. Another example is a data signal that is overprinted as a layer of material, engraved in, or etched onto a substrate, where it may be mixed with other signals applied to the substrate by similar or other marking methods. In these cases, the embedder employs a predictive model of distortion and host signal interference, and adjusts the data signal strength so that it will be recovered more reliably. The predictive modeling can be executed by a classifier that classifies types of noise sources or classes of host image and adapts signal strength and configuration of the data pattern to be more reliable to the classes of noise sources and host image signals that the encoded data signal is likely to be encounter or be combined with.

The output 232 from the Embedder signal typically incurs various forms of distortion through its distribution or use. For printed objects, this distortion occurs through rendering an image with the encoded signal in the printing process, and subsequent scanning back to a digital image via a camera or like image sensor.

Turning to FIG. 15, the signal decoder receives an encoded host signal 240 and operates on it with one or more processing stages to detect a data signal, synchronize it, and extract data.

The decoder is paired with an input device in which a sensor captures an analog form of the signal and an analog to digital converter converts it to a digital form for digital signal processing. Though aspects of the decoder may be implemented as analog components, e.g., such as preprocessing filters that seek to isolate or amplify the data channel relative to noise, much of the decoder is implemented as digital signal processing modules that implement the signal processing operations within a scanner. As noted, these modules can be implemented as software instructions executed within an image scanner or camera, an FPGA, or ASIC, etc.

The detector 242 is a signal processing module that detects presence of the data channel. The incoming signal is referred to as a suspect host because it may not have a data channel or may be so distorted as to render the data channel undetectable. The detector is in communication with a protocol selector 244 to get the protocols it uses to detect the data channel. It may be configured to detect multiple protocols, either by detecting a protocol in the suspect signal and/or inferring the protocol based on attributes of the host signal or other sensed context information. A portion of the data signal may have the purpose of indicating the protocol of another portion of the data signal. As such, the detector is shown as providing a protocol indicator signal back to the protocol selector 244.

The synchronizer module 246 synchronizes the incoming signal to enable data extraction. Synchronizing includes, for example, determining the distortion to the host signal and compensating for it. This process provides the location and arrangement of encoded data elements within the host signal.

The data extractor module 248 gets this location and arrangement and the corresponding protocol and demodulates a data signal from the host. The location and arrangement provide the locations of encoded data elements. The extractor obtains estimates of the encoded data elements and performs a series of signal decoding operations.

As detailed in examples below and in the incorporated documents, the detector, synchronizer and data extractor may share common operations, and in some cases may be combined. For example, the detector and synchronizer may be combined, as initial detection of a portion of the data signal used for synchronization indicates presence of a candidate data signal, and determination of the synchronization of that candidate data signal provides synchronization parameters that enable the data extractor to apply extraction filters at the correct orientation, scale and start location of a tile. Similarly, data extraction filters used within data extractor may also be used to detect portions of the data signal within the detector or synchronizer modules. The decoder architecture may be designed with a data flow in which common operations are re-used iteratively, or may be organized in separate stages in pipelined digital logic circuits so that the host data flows efficiently through the pipeline of digital signal operations with minimal need to move partially processed versions of the host data to and from a shared memory unit, such as a RAM memory.

Signal Generator

FIG. 16 is a flow diagram illustrating operations of a signal generator. Each of the blocks in the diagram depict processing modules that transform the input auxiliary data into a digital payload data signal structure. The input auxiliary data may include, e.g., a Global Trade Item Number (GTIN) developed by GS1. For example, the GTIN may be structured in the GTIN-12 format for UPC codes. Of course, the input auxiliary data may represent other plural bit codes as well. For a given protocol, each block provides one or more processing stage options selected according to the protocol. In processing module 300, the auxiliary data payload is processed to compute error detection bits, e.g., such as a Cyclic Redundancy Check (CRC), Parity, check sum or like error detection message symbols. Additional fixed and variable messages used in identifying the protocol and facilitating detection, such as synchronization signals may be added at this stage or subsequent stages.

Error correction encoding module 302 transforms the message symbols of the digital payload signal into an array of encoded message elements (e.g., binary or M-ary elements) using an error correction method. Examples include block codes, BCH, Reed Solomon, convolutional codes, turbo codes, etc.

Repetition encoding module 304 repeats and concatenates the string of symbols from the prior stage to improve robustness. For example, certain message symbols may be repeated at the same or different rates by mapping them to multiple locations within a unit area of the data channel (e.g., one unit area being a tile of bit cells, as described further below).

Repetition encoding may be removed and replaced entirely with error correction coding. For example, rather than applying convolutional encoding (⅓ rate) followed by repetition (repeat three times), these two can be replaced by convolution encoding to produce a coded payload with approximately the same length.

Next, carrier modulation module 306 takes message elements of the previous stage and modulates them onto corresponding carrier signals. For example, a carrier might be an array of pseudorandom signal elements, with equal number of positive and negative elements (e.g., 16, 32, 64 elements), or other waveform. We elaborate further on signal configurations below.

Mapping module 308 maps signal elements of each modulated carrier signal to locations within the channel. In the case where a digital host signal is provided, the locations correspond to embedding locations within the host signal. The embedding locations may be in one or more coordinate system domains in which the host signal is represented within a memory of the signal encoder. The locations may correspond to regions in a spatial domain, temporal domain, frequency domain, or some other transform domain. Stated another way, the locations may correspond to a vector of host signal features, which are modulated to encode a data signal within the features.

Mapping module 308 also maps a synchronization signal to embedding locations within the host signal, for embodiments employing an explicit synchronization signal. An explicit synchronization signal is described further below.

To accurately recover the payload, the decoder extracts estimates of the coded bits at the embedding locations within each tile. This requires the decoder to synchronize the image under analysis to determine the embedding locations. For images, where the embedding locations are arranged in two dimensional blocks within a tile, the synchronizer determines rotation, scale and translation (origin) of each tile. This may also involve approximating the geometric distortion of the tile by an affine transformation that maps the embedded signal back to its original embedding locations.

To facilitate synchronization, the auxiliary signal may include an explicit or implicit synchronization signal. An explicit synchronization signal is an auxiliary signal separate from the encoded payload that is embedded with the encoded payload, e.g., within the same tile). An implicit synchronization signal is a signal formed with the encoded payload, giving it structure that facilitates geometric/temporal synchronization. Examples of explicit and implicit synchronization signals are provided in our previously cited U.S. Pat. Nos. 6,614,914, and 5,862,260, which are each hereby incorporated herein by reference in their entirety.

In particular, one example of an explicit synchronization signal is a signal comprised of a set of sine waves, with pseudo-random phase, which appear as peaks in the Fourier domain of the suspect signal. See, e.g., U.S. Pat. Nos. 6,614,914, and 5,862,260, describing use of a synchronization signal in conjunction with a robust data signal. Also see U.S. Pat. No. 7,986,807, which is hereby incorporated by reference in its entirety.

Our US Patent Application Publication No. US 2012-0078989 A1, which is hereby incorporated by reference in its entirety, provides additional methods for detecting an embedded signal with this type of structure and recovering rotation, scale and translation from these methods.

Examples of implicit synchronization signals, and their use, are provided in U.S. Pat. Nos. 6,614,914 and 5,862,260, as well as U.S. Pat. Nos. 6,625,297 and 7,072,490, and U.S. patent application Ser. No. 14/724,729 (published as US 2016-0217547 A1), which are hereby incorporated by reference in their entirety.

Signal Embedding in Host

FIG. 17 is a diagram illustrating embedding of an auxiliary signal into host signal. As shown, the inputs are a host signal block (e.g., blocks of a host digital image) (320) and an encoded auxiliary signal (322), which is to be inserted into the signal block. The encoded auxiliary signal may include an explicit synchronization component, or the encoded payload may be formulated to provide an implicit synchronization signal. Processing block 324 is a routine of software instructions or equivalent digital logic configured to insert the mapped signal(s) into the host by adjusting the corresponding host signal sample(s) at an embedding location according to the value of the mapped signal element. For example, the mapped signal is added/subtracted from corresponding a sample value, with scale factor and threshold from the perceptual model or like mask controlling the adjustment amplitude. In implementations with an explicit synchronization signal, the encoded payload and synchronization signals may be combined and then added, or added separately with separate mask coefficients to control the signal amplitude independently.

Applying the method of FIG. 16, the product or label identifier (e.g., in GTIN format) and additional flag or flags used by control logic are formatted into a binary sequence, which is encoded and mapped to the embedding locations of a tile. For sake of illustration, we describe an implementation of a tile having 256 by 256 embedding locations, where the embedding locations correspond to spatial domain embedding locations within an image. In particular, the spatial locations correspond to pixel samples at a configurable spatial resolution, such as 100 DPI or 300 DPI. In this example, we will explain the case where the spatial resolution of the embedded signal is 300 DPI, for an embodiment where the resulting image with encode data is printed on a package or label material, such as a paper, plastic or like substrate. The payload is repeated in contiguous tiles each comprised of 256 by 256 of embedding locations. With these embedding parameters, an instance of the payload is encoded in each tile, occupying a block of host image of about 1.28 by 1.28 inches. These parameters are selected to provide a printed version of the image on paper or other substrate. At this size, the tile can be redundantly encoded in several contiguous tiles, providing added robustness. An alternative to achieving desired payload capacity is to encode a portion of the payload in smaller tiles, e.g., 128 by 128, and use a protocol indicator to specify the portion of the payload conveyed in each 128 by 128 tile. Erasure codes may be used to convey different payload components per tile and then assemble the components in the decoder, as discussed in U.S. Pat. No. 9,311,640, which is hereby incorporated herein by reference in its entirety.

Following the construction of the payload, error correction coding is applied to the binary sequence. This implementation applies a convolutional coder at rate 1/4, which produces an encoded payload signal of 4096 bits. Each of these bits is modulated onto a binary antipodal, pseudorandom carrier sequence (−1, 1) of length 16, e.g., multiply or XOR the payload bit with the binary equivalent of chip elements in its carrier to yield 4096 modulated carriers, for a signal comprising 65,536 elements. These elements map to the 65,536 embedding locations in each of the 256 by 256 tiles.

An alternative embodiment, for robust encoding on packaging employs tiles of 128 by 128 embedding locations. Through convolutional coding of an input payload at rate 1/3 and subsequent repetition coding, an encoded payload of 1024 bits is generated. Each of these bits is modulated onto a similar carrier sequence of length 16, and the resulting 16,384 signal elements are mapped to the 16,384 embedding locations within the 128 by 128 tile.

There are several alternatives for mapping functions to map the encoded payload to embedding locations. In one, these elements have a pseudorandom mapping to the embedding locations. In another, they are mapped to bit cell patterns of differentially encoded bit cells as described in U.S. patent application Ser. No. 14/724,729. In the latter, the tile size may be increased to accommodate the differential encoding of each encoded bit in a pattern of differential encoded bit cells, where the bit cells corresponding to embedding locations at a target resolution (e.g., 300 DPI).

Our U.S. patent application Ser. No. 14/725,399, describes methods for inserting auxiliary signals in areas of package and label designs that have little host image variability. These methods are particularly useful for labels, including price change labels and fresh food labels. These signal encoding methods may be ported to the printing sub-system in scales used within fresh food, deli and meat departments to encode GTINs and control flags for variable weight items in the image of a label, which is then printed by the printer sub-system (typically a thermal printer) on the label and affixed to an item.

For an explicit synchronization signal, the mapping function maps a discrete digital image of the synchronization signal to the host image block. For example, where the synchronization signal comprises a set of Fourier magnitude peaks or sinusoids with pseudorandom phase, the synchronization signal is generated in the spatial domain in a block size coextensive with the 256 by 256 tile (or other tile size, e.g., 128 by 128) at target embedding resolution.

Various detailed examples of encoding protocols and processing stages of these protocols are provided in our prior work, such as our U.S. Pat. Nos. 6,614,914, 5,862,260, and 6,674,876, which are hereby incorporated by reference, and US Patent Publication No. US 2010-0150434 A1 and U.S. patent application Ser. No. 14/725,399, previously incorporated. More background on signaling protocols, and schemes for managing compatibility among protocols, are provided in U.S. Pat. No. 7,412,072, which is hereby incorporated by reference.

One signaling approach, which is detailed in U.S. Pat. Nos. 6,614,914, and 5,862,260, is to map elements to pseudo-random locations within a channel defined by a domain of a host signal. See, e.g., FIG. 9 of U.S. Pat. No. 6,614,914. In particular, elements of a watermark signal are assigned to pseudo-random embedding locations within an arrangement of sub-blocks within a block (referred to as a “tile”). The elements of this watermark signal correspond to error correction coded bits. These bits are modulated onto a pseudo-random carrier to produce watermark signal elements (block 306 of FIG. 16), which in turn, are assigned to the pseudorandom embedding locations within the sub-blocks (block 308 of FIG. 16). An embedder module modulates this signal onto a host signal by increasing or decreasing host signal values at these locations for each error correction coded bit according to the values of the corresponding elements of the modulated carrier signal for that bit.

FIG. 18 is a flow diagram illustrating a method for decoding a payload signal from a host image signal. Implementations of a watermark decoder and watermark processors available from Digimarc Corporation include:

Digimarc Mobile Software Development Kit; and

Digimarc Embedded Systems SDK.

The Embedded Systems SDK is the one typically integrated into scanner hardware.

Corresponding encoder embodiments available from Digimarc Corporation include:

Digimarc Barcode SDKs

Digimarc Barcode Plugin

Returning to FIG. 18, the frames are captured at a resolution preferably near the resolution at which the auxiliary signal has been encoded within the original image (e.g., 300 DPI, 100 DPI, etc.). An image up-sampling or down-sampling operation may be performed to convert the image frames supplied by the imager to a target resolution for further decoding.

The resulting image blocks supplied to the decoder from these frames may potentially include an image with the payload. At least some number of tiles of encoded signal may be captured within the field of view, if an object with encoded data is being scanned. Otherwise, no encoded tiles will be present. The objective, therefore, is to determine as efficiently as possible whether encoded tiles are present.

In the initial processing of the decoding method, it is advantageous to select frames and blocks within frames that have image content that are most likely to contain the encoded payload. From the image passed to the decoder, the decoder selects image blocks for further analysis. The block size of these blocks is set large enough to span substantially all of a complete tile of encoded payload signal, and preferably a cluster of neighboring tiles. However, because the distance from the camera may vary, the spatial scale of the encoded signal is likely to vary from its scale at the time of encoding. This spatial scale distortion is further addressed in the synchronization process.

For more on block selection, please see co-pending U.S. application Ser. No. 14/332,739, published as US 2015-0030201 A1, which are each hereby incorporated by reference in its entirety.

Please also see provisional application 62/174,454, filed Jun. 11, 2015, which is hereby incorporated by reference, for more on block selection where processing time is more limited.

The first stage of the decoding process filters the image to prepare it for detection and synchronization of the encoded signal (402). The decoding process sub-divides the image into blocks and selects blocks for further decoding operations. For color images, a first filtering stage converts the input color image signal (e.g., RGB values) to a color channel or channels where the auxiliary signal has been encoded. See, e.g., U.S. Pat. No. 9,117,268, which is hereby incorporated herein by reference in its entirety, for more on color channel encoding and decoding. For an image captured under red illumination by a monochrome scanner, the decoding process operates on this “red” channel sensed by the scanner. Some scanners may pulse LEDs of different color to obtain plural color or spectral samples per pixel as described in our Patent Application Publication No. US 2013-0329006 A1, which is hereby incorporated by reference.

A second filtering operation isolates the auxiliary signal from the host image. Pre-filtering is adapted for the auxiliary signal encoding format, including the type of synchronization employed. For example, where an explicit synchronization signal is used, pre-filtering is adapted to isolate the explicit synchronization signal for the synchronization process.

In some embodiments, the synchronization signal is a collection of peaks in the Fourier domain. Prior to conversion to the Fourier domain, the image blocks are pre-filtered. See, e.g., LaPlacian pre-filter in U.S. Pat. No. 6,614,914. A window function is applied to the blocks and then a transform to the Fourier domain, applying an FFT. Another filtering operation is performed in the Fourier domain. See, e.g., pre-filtering options in U.S. Pat. Nos. 6,988,202, 6,614,914, and 9,182,778, which are hereby incorporated by reference in their entirety.

For more on filters, also see U.S. Pat. No. 7,076,082, which is hereby incorporated by reference in its entirety. This patent describes a multi-axis filter, e.g., an oct-axis filter. Oct axis compares a discrete image sample with eight neighbors to provide a compare value (e.g., +1 for positive difference, −1 or negative difference), and sums the compare values. Different arrangements of neighbors and weights may be applied to shape the filter according to different functions. Another filter variant is a cross shaped filter, in which a sample of interest is compared with an average of horizontal neighbors and vertical neighbors, which are then similarly summed.

Next, synchronization process (404) is executed on a filtered block to recover the rotation, spatial scale, and translation of the encoded signal tiles. This process may employ a log polar method as detailed in U.S. Pat. No. 6,614,914 or least squares approach of U.S. Pat. No. 9,182,778, to recover rotation and scale of a synchronization signal comprised of peaks in the Fourier domain. To recover translation, the phase correlation method of U.S. Pat. No. 6,614,914 is used, or phase estimation and phase deviation methods of U.S. Pat. No. 9,182,778 are used.

Alternative methods perform synchronization on an implicit synchronization signal, e.g., as detailed in Ser. No. 14/724,729.

Next, the decoder steps through the embedding locations in a tile, extracting bit estimates from each location (406). This process applies, for each location, the rotation, scale and translation parameters, to extract a bit estimate from each embedding location (406). In particle, as it visits each embedding location in a tile, it transforms it to a location in the received image based on the affine transform parameters derived in the synchronization, and then samples around each location. It does this process for the embedding location and its neighbors to feed inputs to an extraction filter (e.g., oct-axis or cross shaped). A bit estimate is extracted at each embedding location using filtering operations, e.g., oct axis or cross shaped filter (see above), to compare a sample at embedding locations with neighbors. The output (e.g., 1, −1) of each compare operation is summed to provide an estimate for an embedding location. Each bit estimate at an embedding location corresponds to an element of a modulated carrier signal.

The signal decoder estimates a value of each error correction encoded bit by accumulating the bit estimates from the embedding locations of the carrier signal for that bit (408). For instance, in the encoder embodiment above, error correction encoded bits are modulated over a corresponding carrier signal with 16 elements (e.g., multiplied by or XOR with a binary anti-podal signal). A bit value is demodulated from the estimates extracted from the corresponding embedding locations of these elements. This demodulation operation multiplies the estimate by the carrier signal sign and adds the result. This demodulation provides a soft estimate for each error correction encoded bit.

These soft estimates are input to an error correction decoder to produce the payload signal (410). For a convolutional encoded payload, a Viterbi decoder is used to produce the payload signal, including the checksum or CRC. For other forms of error correction, a compatible decoder is applied to reconstruct the payload. Examples include block codes, BCH, Reed Solomon, Turbo codes.

Next, the payload is validated by computing the check sum and comparing with the decoded checksum bits (412). The check sum matches the one in the encoder, of course. For the example above, the decoder computes a CRC for a portion of the payload and compares it with the CRC portion in the payload.

At this stage, the payload is stored in shared memory of the decoder process. The recognition unit in which the decoder process resides returns it to the controller via its interface. This may be accomplished by various communication schemes, such as IPC, shared memory within a process, DMA, etc.

II. Robustness Masks (Maps) and Swipe Metrics

One problem with digital watermarking product packages and other physical objects is obtaining visibility that is subjectively pleasing to a graphic designer and others. For example, a graphic designer may be hesitant to introduce too much “noise” into a product's design. The term “noise” refers to visible artifacts in a spatial domain that may be created when the design is transformed to carry an encoded signal, e.g., steganographic encoding or digital watermarking. These artifacts may sometimes have a noise-like, grainy or other visible appearance. As a result, the designer may crank down digital watermark signal strength (or “gain”) or remove the watermarking from some design areas altogether. This may result in a loss of signal robustness, e.g., embedded watermarks carried in the product package may not be detectable in some or all areas of the package surface. We refer to such a package as a low-gain design. (A low-gain design may include areas without watermarking, areas with low-signal gain, perhaps along with some design areas that are robustly embedded.)

Now imagine such a low-gain design in a retail checkout scenario. For example, please see assignee's U.S. Pat. No. 9,224,184, which is hereby incorporated herein by reference in its entirety. In this setting packages move along at retail checkout, e.g., on a conveyor or with employee-assist. A watermark detector analyzing image data representing a low-gain design may not be able to detect watermarks from the image data. A non-detect may result in a slowed check-out process, requiring human intervention or other process to enter the product's ID code.

Low-gain designs need to be discovered prior to press runs. For example, tens of thousands of packages can be printed (e.g., on offset or digital printing presses) before it's realized that a watermarked package design is a low-gain type. At the printing press stage, it is often too late to “stop the presses” to create new printing plates and roll out a new design, one having a higher encoded signal strength.

Aspects of this disclosure address this problem by predicting embedding strength and/or detectability across a printed product package from the digital design itself. If the predicted strength falls short of a predetermined threshold or other metric the digital design can be re-embedded, flagged for inspection and/or redesigned, etc. A mapping (e.g., a heat map or robustness map representing encoded signal detectability) may be generated to visually indicate predicted watermark readability across the image surface. Moreover, strength across a package's scanning length or swipe path (e.g., horizontal and/or vertical swipe paths) can be quantified and adjusted, if needed.

As discussed above an encoded signal, e.g., digital watermarking, may include multiple components. For example, digital watermarking may include a synchronization component (e.g., a reference pattern) and a message (or payload) component. These components may be combined in some domain (e.g., transform domain) to form a watermark signal. In some cases the synchronization component is introduced into a host signal (e.g., an image or audio signal) prior to introduction of the message component.

The synchronization component can be utilized during signal detection. An embedded host signal undergoes various transformations, such as conversion to and from an analog domain (e.g., offset or digital printing and then image capture of the printed design). Using parameters from a watermark embedder (e.g., the reference pattern), a watermark detector may perform a series of correlations or other operations on captured imagery to detect the presence of a digital watermark. If it finds a watermark, it may determine its orientation within the host signal.

Using the orientation, if necessary, the watermark detector may extract or decode the message. Some implementations do not perform correlation, but instead, use some other detection process or proceed directly to extract the watermark signal.

Watermark detectors can be designed in many ways. One design utilizes stages, where a synchronization component is evaluated in a first stage and if it meets certain thresholds (e.g., based on correlation with a reference pattern) it passes onto a second stage. The message component can be evaluated and read in the second stage. Of course, additional stages can be added, e.g., pre-filtering stages and various strength check stages.

There are many types of synchronization components that may be used with the present technology.

For example, a synchronization signal (sometimes called a “reference signal”) may be comprised of elements that form a circle in a particular domain, such as the spatial image domain, the spatial frequency domain, or some other transform domain. Assignee's U.S. Pat. No. 7,986,807, which is hereby incorporated herein by reference in its entirety, considers a case, e.g., where the elements are impulse or delta functions in the Fourier magnitude domain. The reference signal comprises impulse functions located at points on a circle centered at the origin of the Fourier transform magnitude. These create or correspond to frequency peaks. The points are randomly scattered along the circle, while preserving conjugate symmetry of the Fourier transform. The magnitudes of the points are determined by visibility and detection considerations. To obscure these points in the spatial domain and facilitate detection, they have known pseudorandom phase with respect to each other. The pseudorandom phase is designed to minimize visibility in the spatial domain. In this circle reference pattern example, the definition of the reference pattern only specifies that the points should lie on a circle in the Fourier magnitude domain. The choice of the radius of the circle and the distribution of the points along the circle can be application specific. For example, in applications dealing with high resolution images, the radius can be chosen to be large such that points are in higher frequencies and visibility in the spatial domain is low. For a typical application, the radius could be in the mid-frequency range to achieve a balance between visibility requirements and signal-to-noise ratio considerations.

Another example is found in Assignee's U.S. Pat. No. 6,614,914, which is hereby incorporated herein by reference in its entirety. There, a synchronization component (or “orientation pattern”) can be comprised of a pattern of quad symmetric impulse functions in the spatial frequency domain. These create or correspond to frequency peaks. In the spatial domain, these impulse functions may look like cosine waves. An example of an orientation pattern is depicted in FIGS. 10 and 11 of the '914 patent.

Another type of synchronization component may include a so-called Frequency Shift Keying (FSK) signal. For example, in Assignee's U.S. Pat. No. 6,625,297, which is hereby incorporated herein by reference in its entirety, a watermarking method converts a watermark message component into a self-orienting watermark signal and embeds the watermark signal in a host signal (e.g., imagery, including still images and video). The spectral properties of the FSK watermark signal facilitate its detection, even in applications where the watermarked signal is corrupted. In particular, a watermark message (perhaps including CRC bits) can be error corrected, and then spread spectrum modulated (e.g., spreading the raw bits into a number of chips) over a pseudorandom carrier signal by, e.g., taking the XOR of the bit value with each value in the pseudorandom carrier. Next, an FSK modulator may convert the spread spectrum signal into an FSK signal. For example, the FSK modulator may use 2-FSK with continuous phase: a first frequency represents a zero; and a second frequency represents a one. The FSK modulated signal is can be applied to rows and columns of a host image. Each binary value in the input signal corresponds to a contiguous string of at least two samples in a row or column of the host image. Each of the two frequencies, therefore, is at most half the sampling rate of the image. For example, the higher frequency may be set at half the sampling rate, and the lower frequency may be half the higher frequency.

When FSK signaling is applied to the rows and columns, the FFT magnitude of pure cosine waves at the signaling frequencies produces grid points or peaks along the vertical and horizontal axes in a two-dimensional frequency spectrum. If different signaling frequencies are used for the rows and columns, these grid points will fall at different distances from the origin. These grid points, therefore, may form a detection pattern that helps identify the rotation angle of the watermark in a suspect signal. Also, if an image has been rotated or scaled, the FFT of this image will have a different frequency spectrum than the original image.

For detection, a watermark detector transforms the host imagery to another domain (e.g., a spatial frequency domain), and then performs a series of correlation or other detection operations. The correlation operations match the reference pattern with the target image data to detect the presence of the watermark and its orientation parameters.

Yet another synchronization component is described in assignee's U.S. Pat. No. 7,046,819, which is hereby incorporated by reference in its entirety. There, a reference signal with coefficients of a desired magnitude is provided in an encoded domain. These coefficients initially have zero phase. The reference signal is transformed from the encoded domain to the first transform domain to recreate the magnitudes in the first transform domain. Selected coefficients may act as carriers of a multi-bit message. For example, is an element in the multi-bit message (or an encoded, spread version of such) is a binary 1, a watermark embedder creates a peak at the corresponding coefficient location in the encoded domain. Otherwise, the embedder makes no peak at the corresponding coefficient location. Some of the coefficients may always be set to a binary 1 to assist in detecting the reference signal. Next, the embedder may assign a pseudorandom phase to the magnitudes of the coefficients of the reference signal in the first transform domain. The phase of each coefficient can be generated by using a key number as a seed to a pseudorandom number generator, which in turn produces a phase value. Alternatively, the pseudorandom phase values may be computed by modulating a PN sequence with an N-bit binary message. With the magnitude and phase of the reference signal defined in the first transform domain, the embedder may transform the reference signal from the first domain to the perceptual domain, which for images, is the spatial domain. Finally, the embedder transforms the host image according to the reference signal.

A correlation based technique can be used to help locate and decode the watermark signal.

To evaluate watermark characteristics in a digital design and use such to predict detectability on a corresponding printed object, we generate detectability measures that correspond in various ways to the embedded watermark signal (e.g., the synchronization component and/or the message component). The premise is that if we can estimate the detectability measures in a digital design, then we can estimate the chance of a successful read when that digital design is printed, e.g., on a product package, scanned (e.g., image capture of the printed product package) and decoded.

Some of our detectability measures are described and applied below.

For this analysis we used 300 digital test images each including a 2 by 2 inch patch. The image size and sample size of these images is not critical, but is useful to help illustrate aspects of this disclosure. Two examples of test image patches are shown in FIG. 1a and FIG. 1 b.

Each of the digital test images is embedded with digital watermarking including a reference pattern and a message. Some of the images include so-called “sparse” digital watermarking, e.g., as described in assignee's U.S. patent application Ser. No. 14/725,399, filed May 29, 2015, which is hereby incorporated herein by reference in its entirety. Some images include digital watermarking as generally discussed, e.g., in assignee's US Published Patent Application No. US 2015-0156369 A1, which is hereby incorporated herein by reference in its entirety.

These embedded, test images are then printed, e.g., using an Epson 4900 printer with a GRACoL profile and semi-matte paper. An image capture device, e.g., the Datalogic 9800i (including a horizontal and vertical camera) with a video feed enabled was used to capture 100 digital images of each printed test image for each camera (so a total of 200 digital images or “scans” were captured for each test image). The printed images were each positioned in a way that allows maximum detection for the given camera. Additionally, each printed image was placed on a plate (again, in the most readable position on the scanner) and automatically swiped 400 times in front of each camera. A robotic arm (“robot”) that moves the printed images in front of the cameras was used for this other image capture. All captured, digital images were saved and evaluated as discussed below.

A watermark detector analyzed each digital image captured from the scans. The following detection measures were determined.

Reference Pattern Strength (RPS):

For each captured digital image we determined its RPS, which, e.g., comprises a sum of bounded frequency domain signal peak strengths. This value can be normalized, e.g., between 0 and 2, for each peak strength. RPS mean from the test images are shown in FIG. 2a , which has a rather large standard deviation.

Linear Reference Pattern Strength (LRPS):

Here we determine the average of unbounded peak strengths. For example, we compare each reference pattern peak to its neighbors (e.g., 4, 6 or 8 neighbors). In other words, how does this peak compare to its neighborhood, e.g., in terms of magnitude or other measure. The subject peak value can be divided by the average value of its neighbors. Its measure includes a better behaved standard deviation compared to the RPS, as seen in FIG. 2 b.

Phase Deviation Strength (PDS):

This measure represents how consistent is the phase of reference pattern peaks are relative to translation. It is, however, very sensitive to how well rotation and scale are estimated, which leads to extremely high standard deviation, as shown in FIG. 2 c.

Criterion Strength (CS):

Is a combination of Reference Pattern Strength and Phase Deviation. The value of Criterion Strength can be represented as: Criterion Strength=0.17*RPS−PDS−46.5.

However, since this criterion includes two measures with high variance, as seen in FIG. 2d , it is difficult to estimate the CS.

Message Strength (MS):

One example of message “strength” was described in assignee's U.S. Pat. No. 7,286,685, which is hereby incorporated by reference in its entirety. From the '685 patent, one approach for measuring strength of a message signal is as follows: 1. Use the message payload read from the watermark to re-create the original embedded bit sequence (including redundantly encoded bits from error correction coding) used for the watermark. 2. Convert the original bit sequence so that a zero is represented by −1 and a one is represented by 1.3. Multiply (element-wise) the soft-valued bit sequence used to decode the watermark by the sequence of step 2.4. Create one or more measures of watermark strength from the sequence resulting in the previous step. One such measure is the sum of the squares of the values in the sequence. Another measure is the square of the sum of the values in the sequence. Other measurements are possible as well. For example, soft bits associated with high frequency components of the watermark signal may be analyzed to get a strength measure attributed to high frequency components. Such high frequencies are likely to be more sensitive to degradation due to photocopying, digital to analog and analog to digital conversion, scanning and re-printing, etc. 5. Compare the strength measures to thresholds . . . .

Another method, introduced in this patent document, is a correlation metric based on a watermark's message signature. With reference to FIG. 3, a signature is generated based on a payload or message, e.g., carrying a GTIN, UPC, identifier or some other plural-bit message. While there are specific bit numbers illustrated in FIG. 3, the present disclosure is not so limited, as we may include many more or less bits for the various payload components. The payload can be combined with CRC bits, and the combination can be error corrected, e.g., with a convolutional encoder. A payload header can be added to help distinguish the type or format of the watermarking or to indicate a payload version. The resulting bit string can be used as the signature.

Each bit of the signature can be placed redundantly in a digital image. For example, in a tile design, where watermark tiles (e.g., a 128×128 message tile) are tiled across an image or image area, each bit of the signature can be mapped, e.g., 8-16 times, to a pseudo-random location (with pseudo-random sign) to create a message tile.

The signature is unique for each GTIN and the Message Strength (MS) and can be represented as a relationship between the embedded signature and the sign of the decoded signature:

${MS} = {\sum\limits_{i = 1}^{1024}{{Embedded}\;{{Signature}(i)}*{{sign}\left( {{Decoded}\;{{Signature}(i)}} \right)}}}$

This measure has relatively small standard deviation, as seen in FIG. 2 e.

Additional signal strength metrics are discussed in U.S. Pat. No. 7,054,461, e.g., a so-called Power Ratio and Payload Recovery Assessment. The U.S. Pat. No. 7,054,461 is hereby incorporated herein by reference in its entirety.

The power ratio metric measures, e.g., the degradation of a watermark signal (e.g., a synchronization component) at selected frequencies.

The Payload Recovery Assessment measures watermark strength, including the degree of correlation between a synchronization component and a detected signal, and a measure of symbol errors in raw message estimates. One way to measure the symbol errors is to reconstruct the raw message sequence using the same error correction coding process of the embedder on the valid message extracted from the watermark. This process yields, for example, a string of 1000 binary symbols, which can be compared with the binary symbols estimated at the output of a spread spectrum demodulator. The stronger the agreement between the reconstructed and detected message, the stronger the watermark signal.

Thanks to their small variance leading to easier predictability, Linear Reference Pattern Strength (LRPS) and the Message Strength (MS) are selected in a first example as detectability measures. These measures are first correlated with an actual watermark detector behavior. For example, LPRS is correlated with passing stage 1 (or “first stage”) of a detection process. Here, as mentioned above, stage 1 includes a synchronization stage, where a reference pattern can be detected or correlated relative to a known reference pattern. FIG. 4 shows a histogram of LRPS values based on whether a watermark tile passed the first stage. The blue lines indicate scanned images having an LRPS that passed the first stage, and the red lines indicate scanned images having an LRPS that did not pass the first stage. A decision can be made to establish a threshold (vertical dashed line) indicating that the first stage threshold is passed if the LRPS is larger than a predetermined LRPS value. While this threshold need not be held completely, however, it greatly simplifies the criteria. For example purposes we set a strength threshold, T, of 1.27, so T_(LRPS)=1.27. Of course, the threshold can be moved up, e.g., 1.5 to ensure less false positives, or moved down, e.g., 1.1 if more false positives are acceptable.

FIG. 5 shows a histogram plot for Message Strength. Again, the blue lines represent those images with a certain Message Strength resulting in successful message reads, and the red lines represent those images with a certain Message Strength resulting in unsuccessful message reads. A strength threshold (vertical dashed line in FIG. 5) can be determined to represent when a watermark message is read. For example purposes we set of strength threshold, T, of 280 so T_(MS)=280. This value can change to accommodate more or less false positive tolerance.

Using this criteria for LRPS and MS, we can predict that a digital watermark is likely to be successfully read when:

1. LPRS>T_(LPRS); and

2. MS>T_(MS).

This is all fine and good for data that has been collected in a “design-print-image capture-detect” scenario. But, recall that we are interested in predicting how a digital image, once embedded with digital watermarking and before printing, will be detectable after printing. So how do we apply our above thresholds and detectability measures to a digital image, one that once watermarked will be used to guide printing, e.g., on physical product packaging?

One approach is to transform watermarked digital images so that the transformed, watermarked images correspond to what an image capture device sees, and to what a watermark detector ultimately analyzes when detecting a watermark from captured imagery. In a transformed, watermarked image case, we can extract the LRPS and MS statistics using a watermark detector on the transformed, watermarked digital image. Using these detectability measures and the thresholds discussed earlier, we can predict whether a digital watermark will be read once printed on a physical surface. We affectionately call these types of transformations an “attack,” because they degrade the quality of a watermarked digital image.

One improvement is that the whole image pipeline (digital image→watermark embedding→print→color transform (e.g., with a red LED/laser)→image capture→watermark detect) need not be simulated during the attack. Rather, we introduce an attack that would make the LGS and MS statistics generally correspond to the statistics extracted directly from a scanned image.

For example, if we want to estimate robustness using a specific scanner, we can evaluate the attack by comparing the LRPS and MS determined on “attacked” watermarked digital images with the LRPS and MS obtained by printing the images, putting them in front of the scanner and taking image captures from the scanner (e.g., Datalogic's 9800i) for watermark detection.

One example attack for watermarked embedded digital images proceeds as follows. (While this example is geared toward a two-camera system like the Datalogic 9800i, it need not be so.)

-   -   a. Add noise to the original digital artwork. Gaussian noise is         one example. For a two camera system, noise standard deviation         for the vertical camera is approximately 28, and for horizontal         camera is approximately 22.     -   b. For the horizontal camera, blur the digital image. This         simulates a 30-50 degree pre-distortion and resampling. A 7×7         averaging kernel can be used for the blur.     -   c. Down sample the image in a manner similar to watermark         detection. For example, if a watermark detector down samples by         3, then the image can be convolved with a 3×3 averaging kernel         and subsampling.     -   d. For multiple in-plane (yaw) rotations obtain detectability         measures.

Our initial watermarked, digital image test set was degraded according to the above attack. Referring to FIGS. 6a-6d , results from the images on x axis is the measures extracted from “attacked” digital images. On y axis is the measures extracted from printed/scanned captures of the same (but not attacked) embedded digital. The “×” marks correspond to the so-called “sparse” watermarking mentioned above, and the “+” marks correspond to watermarking introduces through changes in process colors, e.g., as discussed in assignee's US Published Patent Application No. US 2015-0156369 A1. Ideally, we want the values to be correlated, therefore, as close to diagonal as possible. FIGS. 6a and 6b show closely correlated values.

With reference to FIG. 6c and FIG. 6d , examples of wrong fits includes the “+” markers on the bottom of the horizontal camera attacks. We found that these data points correspond to very dark images which are unreadable using our test scanner. Thus, the simulated detectability measures are much higher than the captured ones. This is a result of our test scanner, and not of the attack.

Given a digital watermarked image, we can now attack it to obtain estimated LRPS and MS detectability measures. For example, after an attack, a digital watermark detector analyzes the attacked image and determines detection measures, e.g., on a per tile basis, throughout the image. We can simplify a detection prediction by saying that LRPS and MS measure must be larger than some threshold in order to detect. This can be written in probabilistic form as: P(detection)=P(LRPS>T _(LRPS))·P(MS>T _(MS))

For simplification, and letting LRPS=LGS (just a different name) we can assume that the estimation error of LGS and MS follow a normal distribution N with zero mean and standard deviation measured from the plots above, σ_(LGS)=0.3, σ_(MS)=100 Finally, the probability of detection can be written as:

P(,) = P(lgs > T_(LGS); lgs ∼ 𝒩(  σ_(LGS)²)) ⋅ P(ms > T_(MS); ms ∼ 𝒩(  σ_(MS)²)) = (1 − cdf(1.27;   0.3²)) ⋅ (1 − cdf(280;   100²))

With knowledge of the robot arm speed, the test image size and the watermark tile configuration, we can simulate a swipe path moving through the watermark tiles. By doing multiple simulations with different swipe starting locations, we can obtain the average swipe rate. Swipe rate is determined since printed product packages are often “swiped” in front of a camera during retail check out. So the camera (and watermark detector) sees a swath of imagery or a stripe of imagery across a package.

Probability of swipe detection using local read rates P(

^((f,s)),

) from frame f of swipe s:

P ⁡ ( swipe ⁢ ⁢ s ) = 1 - ∏ frames ⁢ ⁢ f ⁢ ⁢ in ⁢ ⁢ swipe ⁢ ⁢ s ⁢ ⁢ ( 1 - P ⁡ ( ( f , s ) , ( f , s ) ) )

Final swipe detection rate is computed as an average over all (or a subset of all) simulated swipes s along a path, e.g., horizontal or vertical path or for an entire image.

With reference to FIG. 7, after attack and watermark detection per tile or area, a heat map can be generated showing the overall probability of watermark detection for the image. That is, probabilities can be assigned color values, and the color values are shown in a spatial map form relative to the image. In FIG. 7, red areas are those having a low-gain or low probability of watermark detection once the digital image (watermarked, but without the attack) is printed, scanned and analyzed with a watermark detector. The blue areas are those having a high chance of detection from the printed digital image (watermarked, but without the attack). The scale moves through other colors between red and blue.

FIG. 7 also includes a horizontal and vertical color bar. The vertical bar shows swipe read probability if a swipe path is taken horizontally through the image. For example, at a particular location, the vertical color bar corresponds to a virtual swipe along the horizontal dashed line. (As discussed above, the probability of tiles along the path can be averaged to determine the overall path probability. Alternatively, extreme probabilities can be weighted or discounted depending on tolerances for a particular application.) Here, along the horizontal dashed path, its light blue, indicating a likely read. The horizontal color bar also shows the collective probability for a vertical swipe path (e.g., along the dashed vertical line). An overall score can be determined for the digital image (e.g., “63%” shown in the lower right hand corner of FIG. 7). A threshold can be set, e.g., somewhere between 60-90% (e.g., below 90%, or below 85%, or below 80%, or below 75%, or below 70% or below 65%, etc.) and any image falling below such threshold will be flagged for or automatically re-embedded, e.g., using a stronger embedding strength or different type of encoding protocol.

One or more graphical user interfaces (GUI) may be configured to control display of the heat map on a display screen, e.g., computer monitor, touchscreen display, smartphone display, OLED display, TV screen, projection, etc. The GUIs may be configurable to allow emphasis of embedded areas that have low-gain or high robustness. In other cases, the GUI allows a user to highlight areas for re-embedding, e.g., on a touchscreen display that a user may trace an area for re-embedding. The GUI keeps track of the trace and corresponds such to the digital image, which can be used as a mask for re-embedding digital watermarking.

III. Construction of Visual Maps Using Robustness as a Mask

A heat map or robustness map (e.g., as shown in FIG. 7) can be used as an image mask to visually evidence digital watermark robustness or detectability throughout an image.

For example, consider the image shown in FIG. 9A. This particular image represents a product package design, e.g., for delicious Tomato Sauce. The illustrated design includes digital watermarking embedded therein. The watermarked design can be analyzed, e.g., as discussed above in Section II, to determine watermark signal detectability.

A robustness map can be generated corresponding to the detectability results. For example, and with reference to FIG. 9B, a robustness map can be generated to show areas having relatively stronger detectability. In this case (unlike FIG. 7), the white areas show those areas having sufficient detectability, e.g., based on a threshold or relative to other image areas having lower detectability (e.g., shown in black color in FIG. 9B). In particular, the white areas may indicate a high probability of the digital watermarking being read by a red LED Point of Sale scanner, whereas dark areas may indicate low probability of the watermark being read by the red LED scanner. Assignee's U.S. patent application Ser. No. 14/616,686, filed Feb. 7, 2015 (published as US 2015-0156369 A1), which are both incorporated herein by reference in their entireties, discuss various spectral characteristics of typical red LED scanners.

One illustrative process operates on a watermarked image (e.g., FIG. 9A) and its corresponding robustness map (e.g., 9B). The following steps can be performed in image editing and design suites, e.g., Adobe's Photoshop or Illustrator, etc. Of course, these steps can be automated with software code, scripts, functions, circuitry and/or modules.

1. Convert the original digital watermarked image in FIG. 9A to greyscale.

2. Modify the greyscale image's opacity to some percentage less than 100%, e.g., to between 20% and 95%, to between 40% and 80%, to between 50% and 70 percent, etc.

3. Overlay the modified greyscale image onto a white (or lightly colored) background. (FIG. 10 shows a greyscale version of FIG. 9A with 50% opacity overlaid onto a white background.)

4. Mask the original embedded image using a robustness map indicating the robustness of the digital watermarking at each pixel, groups of pixels or image areas in the original image. FIG. 11 shows a result of the original watermarked image FIG. 9A masked using the FIG. 9B robustness map. The resulting white areas in FIG. 11 correspond to the black areas in FIG. 9B, e.g., those have low or no watermark robustness.

5. Overlay the masked image (e.g., FIG. 11) on top of the modified greyscale image (FIG. 10). FIG. 12 shows the result of this overlaying. This final robustness image (or “signal detection map”) indicates those areas having a high probability of digital watermark detection in original design color and indicates in grey those areas having a low (or relatively lower) probability of the digital watermark detection.

Such a result (e.g., FIG. 12) provides a designer with a practical and visual example of how a package will be machine-read, including a more realistic design presentation compared, e.g., to the FIG. 7 heat map. Thus, this signal detection map (FIG. 12) is helpful to see where the digital watermark is strong (by showing the original design color) and where it is weak (shown in grey or other color); and it also allows inspection of modification artifacts (e.g., signal embedding artifacts) to be assessed directly on signal detection map itself.

In some implementations, we add a swipe component to the FIG. 12 final robustness image. That is, we provide horizontal and vertical swipe information (like in FIG. 7) to allow a designer a fuller picture of how the digital watermarking will be read by a scanner. For example, FIG. 13 shows horizontal (grey result) and vertical (color result) swipe information. The resulting swipe information can, e.g., keep with the final robustness image color scheme (e.g., grey for hard to read areas and original color for detectable areas). Or the resulting swiping information can be a totally different color, e.g., red for stop, no good; and green for good to go, etc.

While we have used specific colors to represent various results (e.g., original color=robustness, grey=no or less robustness) other colors can be used instead. For example, white or black can be used for low/no robustness areas.

IV. Detecting Multiple Different Codes within Imagery

The presence of multiple, different encoded signals (e.g., steganographic codes, barcodes, etc.) in imagery can sometimes create havoc, e.g., in the retail packaging space where a consumer is typically charged according to the presence of a code. Take an easy example of a 2 different 1D barcodes printed on a box of cereal, with the first 1D barcode corresponding to a box of “Sugar O's” and the second 1D barcode corresponding to a box of “Honey Sugar O's”. A consumer at checkout could be charged twice (once for a box of Sugar O's and another for a box of Honey Sugar O's) if both of the 1D barcodes are scanned, even though the consumer only intended to purchase 1 box of cereal. The consumer gets charged twice, and a retail store's inventory management system now has erroneous information.

Now consider some progressively harder examples. In a first case, a box of cereal includes an encoded signal redundantly covering some or a majority of the surface area of a box of cereal. Each instance of the encoded signal carries a payload, e.g., UPC payload 1. Examples of encoded signals may include, e.g., steganographic encoding or digital watermarking, or some other encoding scheme. The cereal box also includes a 1D UPC barcode. The 1D UPC barcode also carries UPC payload 1. No disagreement between the 1D UPC barcode code and the encoded signal code, so the consumer is only charged once. (See, e.g., assignee's U.S. Provisional Patent Application No. 62/322,193, filed Apr. 13, 2016, which is hereby incorporated herein by reference in its entirety, for related solutions for conflicting/similar codes including wait times between decoded codes.)

In a second case, each instance of the encoded signal carries the same payload as above, UPC payload 1. The UPC 1D barcode, however, is different and conveys UPC payload 2. Here, a conflict exists and a retailer runs the risk of charging a customer twice. Moreover, the conflict between encoded signal and the 1D barcode may indicate a larger problem, e.g., a manufacturing issue such as mislabeling or a wrong product altogether.

In a third case, various different encoded signals are provided on the package, with a first encoded signal including UPC payload 1, and a second encoded signal including UPC payload 2. A conflict results here again.

It's imperative that such conflicts are discovered early in the design process, preferably before printing and distribution.

Let's look at a typical workflow for a product package. For example, a workflow may include one or more of the following steps:

1) Receive digital package files from, e.g., via a secure FTP.

2) Pre-Flight to determine that we have all info. Pre-flight is a term used to describe a preliminary step that evaluates received information, and may include reformatting, decompressing files, and an overall evaluation whether the received digital page files can be assembled into a printable package. Package artwork is typically represented by a collection of files in a variety of different formats e.g., Bitmaps (*.tiff, *psd, etc.), vector imagery (*.ps, *.ai, etc.), and fonts (*.abf, *.ttf, etc.). A final rendered packaged can be “built” using the aforementioned files using a variety of different strategies, from a 1-layer bitmap to numerous layers of vector and bitmap imagery utilizing multiple fonts.

3) Enter Package/Retailer/Printer/Supplier in CRM system, e.g., Microsoft Dynamics CRM (not shown). Optionally, the materials may include an XML file which can be used to automatically enter the information. In this case, a manual check will help ensure accuracy.

4) Assign to Teams. For example, different tasks can be assigned to different work stations, or to available operators. An operator queue can be examined to determine availability.

5) Create an identity file in an identity management system (e.g., housed in the cloud) and associate the GTIN. The creation and management of these services can be accomplished through a web-portal to the identity management system or programmatically through Web APIs. If the packaging materials includes a 1D barcode number, e.g., in a GTIN format, this information can be obtained and provided as used as the encoded signal payload or part of a payload, or to a storage location at which an encoded signal payload will point to.

6) Review Files—Different Classifications. These classification may include assignment of package embedding difficulty. This may prompt additional resources or billing requirements.

7) Print-out Initial Client Proof.

8) Embed encoded signal.

9) Print Watermarked Proof

10) Test on POS Scanner. This is a preliminary test to see if the proof will read.

11) Assemble Package for Manual Test

12) Manual Test. This can be a detailed process, where each package face is tested, e.g., at different reading angles. For example, each side is tested on a POS scanner with a vertical camera and a horizontal camera. The package is passed over the scanner, e.g., 2, 4 or 8 times per side and then number of reads is recorded. The side is rotated, e.g., 90 degrees and the process is repeated for that side, rotated again and retested, etc. Each package side can be so tested and the results recorded. A grade can be assigned based on successful reads. Of course, the process is benefited from automation where a package is passed in front of a scanner, e.g., with a robot arm, conveyor belt or some other movement mechanism.

13) Complete QC Checklist

-   -   13a) compare results of digital grade and manual grade; decide         whether to accept or refine embedded package.

14) Send Approved file to Customer or Printer via FTP

15) At the Printer, enter file into system (e.g., ESKO Packaging Management software, provided by ESKO, with offices worldwide including in Miamisburg, Ohio 45342 (USA)).

16) Various ink and printing pre-checks.

17) Ready the file for printing plate manufacture, including allowing the client or pre-press designer a final review (e.g., 1-bit .tif file) review. Approval or change request received.

18) Plate manufacture (e.g., for an offset press) based on the design file.

19) Print run—print the design file.

So how do code conflicts surface? For example, in step 5 an encoded signal preferable duplicates a provided GTIN payload from a 1D barcode.

A few sources of conflicts may include, e.g., operator error (erroneous data entry), using the wrong 1D barcode file, etc. At the printing press stage (e.g., steps 15-19), product design families may be associated in a packaging management system. A product design family may include, e.g., a line of dish soap which includes related designs, with some design elements being identical and others being unique. For example, the background across the family designs may include a design element in a Cyan color channel that doesn't change from family member to family member. Yet, each family member may include other design elements that are unique (e.g., color changes to reflect dish soap fragrance). In this dish soap example, the printer may realize an advantage by reusing the common Cyan element printing plate across the entire family. If the common plate is manufactured based on a first design file, with a first encoded signal represented in the first design file, then each package printed with this plate will include the first encoded signal in the common plate area (e.g., background). But if each family member includes a unique GTIN to represent that it's a different product than the family, and other printing plates are manufactured for these unique design elements and unique GTIN, each family member will also include different encoded signals. So a package printed with the common Cyan plate (including the 1^(st) encoded signal) and a unique family plate (e.g., including a 2^(nd) encoded signal) will have at least two conflicting codes printed thereon.

If these types of code conflict problems are discovered after printing, the printer or customer may lose tens of thousands of dollars, if not more.

We have developed a “scanner verifier” to help catch such potential code conflicts before printing plate manufacture, or before complete print press runs. Thus, the scanner verifier can be used prior to printing plate manufacture (e.g., just prior to step 18, above) or as the first sheets start rolling off the presses in step 19.

The scanner verifier preferably includes a plurality of code detectors. For example, it may include a signal decoder, e.g., as discussed relative to FIGS. 15 and 18 and a barcode decoder, e.g., a 1D and/or 2D barcode decoder.

Suitable 1D and 2D barcode decoders include, e.g., the open source ZXing (“Zebra Crossing”) barcode project. Of course, other 1D and/or 2D barcode decoders could be used instead. The ZXing barcode decoders support 1D product codes (UPC-A, UPC-E, EAN-8 and EAN-13), 1D industrial codes (e.g., Code 39, Codabar, etc.) and 2D codes (e.g., QR Code, Data Matrix, etc.). The ZXing source code can be obtained at http://github.com/zxing/zxing/releases. The ZXing decoder is configured to accept scanline data. (A scanline includes a row of image data, e.g., pixel values or binarizied versions of the pixel values.) How the scanlines are generated, however, is left to the implementation.

The scanner verifier also preferably includes a code comparator and a display generator.

A scanner verifier is discussed further with reference to FIGS. 21A and 21B, where an image, e.g., from a design file or from a scanner (e.g., a scanned sheet from a print run) is analyzed in parallel (FIG. 21A) or in series (FIG. 21B). A barcode module 501 analyzes the image to determine whether it contains a barcode, and if so, decodes the barcode and provides the barcode's payload (e.g., representing a GTIN number) to a comparator module 504. Barcode module 501 will also determine whether the image includes two or more barcode (e.g., a 1D and a 2D barcode). Each of the barcode's payloads can be provided if found, optionally along with a spatial image location of the barcode(s). The image is also analyzed by a Decoder Module 502 (also called a “Decoder” in this Section IV) to detect and decode encoded signals. For example, the Decoder Module 502 can be configured as a steganographic or watermark decoder. The Decoder Module 502 outputs a decoded code (e.g., representing a GTIN number), optionally along with a spatial image location for each decoded code, to the Comparator Module 504. The Comparator Module 504 compares payloads from both the Barcode Module 501 and the Decoder Module 502, to see if they match and/or whether there are any code conflicts. The Comparator Module 504 can be configured to compare plural codes from each of the Barcode Module 501 (e.g., compare a 1D with a 2D code) and the Decoder Module 502 (e.g., compare a 1^(st) decoded signal and a 2^(nd) decoded signal), and between all codes regardless of decoder source.

The Comparator Module 504 can output the codes and one or more conflict indicators for display or for a report. The conflict indicators can indicate, e.g., no conflict or information regarding the code conflicts.

Results Module 506 can create information for a graphical user interface for display. Different colors, boxing and/or highlighting can be used to show results of the conflict comparison.

Returning to the Barcode Module 501, scanlines are provided from data representing the image for 1D barcode analysis. For example, data representing the image may include a greyscale or binarizied version of the image. Scanline production and management may be controlled by a Barcode Module 501 sub-module or a different module altogether (a “scanline generator module”). The scanlines typically represent 1 or more pixel row(s) of image data. In some cases, we combine adjacent (top and bottom) scanlines together (e.g., with an AND function) to form a single scanline. A scanline's length can vary, e.g., in one implementation the scanlines are 4 inches long. This means that for a 300 dpi image, a single scan line will contain 1200 pixels. In some cases the scanlines can be overlapped, e.g., 25-75%. For example, a first scanline from a first image row is four inches long and starts at point (0″,0″), with the top left corner of the image being the origin, and ends at (4″,0″). The 2^(nd) scanline starts at (1″, 0″) and runs to (5″, 0″) and so on. If reading a horizontal barcode, the scanline generator module can provide scanlines along the first image pixel row, then skip down the image if no barcode is found. For example, the next scanline may be 10, 20 or 50 rows down the image. The scanline generator module can continue to skip down the image until a barcode is read, or until it reaches the bottom of the image.

If the Barcode Module 501 detects and decodes a barcode from a particular scanline, the scanline generator module preferably provides scanlines from the next n number of rows (n being an integer) below the successfully read scanline. For example, scanlines from the next 3 rows, 5 rows, 10 rows or 20 rows are analyzed to get a read. The Barcode Module 501 can be configured to only output a code if all of the n scanlines (or a predetermined subset of the n scanlines) also generate the same code.

The scanline generator module can also be configured to provide vertical scanlines by using image columns instead of rows. This will help find a barcode that may be rotated at a 90 degree angle within the imagery. Additionally, off-axis (e.g., 45 degree) scanlines can be generated and provided to the Barcode Module 501 for analysis.

Returning to the Decoder Module 502 (also referred to as “Decoder”), the Decoder can be configured to analyze multiple different color separations (or planes) from the image. For example, the image may include Red (R), Green (G), Blue (B) separations or may include Cyan (C), Magenta (M), Yellow (Y), Black (K) separations. The image may also include Spot Color 1 and Spot Color 2, etc. The Decoder can be configured to analyze each of these separations separately looking for encoded signals. In some cases, separations can be combined, perhaps with various weights, to represent, e.g., chrominance (e.g., R−G; or 0.5R−0.5G) or Luminance (e.g., R+G+B; or 0.2126*Rlinear+0.7152*Glinear+0.0722*Blinear, for Luminance (Y linear) in the sRGB specification). The Decoder can then analyze the Chrominance and Luminance channels in search of encoded signals.

In one implementation, the Decoder is configured to analyze image blocks or tiles. With reference to FIG. 19A, an image is segmented into blocks, e.g., 512×512 or 128×128 pixel blocks. (The Decoder may down-sample (or up-sample) the blocks to achieve a native encoded signal resolution. For example, a 512×512 block may be downsampled to a 128×128 block or tile.) While we prefer to block out the entire image as in FIG. 19A, in some cases only a portion of the image is blocked out (FIG. 19B). These blocks can be used for all separations in the image, e.g., the Yellow separation is blocked in the same manner as the Magenta and Cyan separations, etc. In alternative implementations, each separation or groups of separations are blocked separately.

Each block (or a subset of blocks) from each separation can be analyzed by the Decoder to determine whether that block contains an encoded signal code and, if so, may associate the code with a particular block or image spatial area. For example, the blocks can be numbered, tracked or assigned to a spatial location within the image. The code can be assigned to the numbered, tracked or assigned block from which it was decoded, or otherwise assigned to an image spatial area. For example, and with reference to FIG. 19C, the Decoder operates on blocks from one separation (e.g., Magenta or Chrominance) from the FIG. 19B drawing, and may assign decoded codes to spatially oriented blocks. In all likelihood, the results will be stored in a table or memory structure, along with some spatial indicator. (Of course, the Decoder could operate on all image blocks instead, e.g., as from FIG. 19A.) In this example, an encoded signal (ES1) is detected in the majority of blocks as shown in FIG. 19C. The Decoder did not find an encoded signal in three (3) of the blocks, nos. 8, 17 and 19, so these blocks are illustrated as empty. (The Comparator Module 504 would not find a code conflict in this FIG. 19C separation since all of the decoded codes include the same code ES1, e.g., which may correspond to a GTIN number.) With reference to FIG. 19D, the Decoder operates on another image FIG. 19B separation (e.g., Cyan or Luminance). An encoded signal (ES1) is detected and decoded in the majority blocks, but a second encoded signal (ES2) is detected and decoded in two (2) blocks, e.g., block nos. 14 and 19. No encoded signal is found in two (2) of the blocks, nos. 8 and 17, and are illustrated as empty. (The Comparator Module 504 would find a code conflict within this separation since two different codes, ES1 and ES2, are decoded. Additionally, the Comparator Module 504 may indicate a conflict between the two separations, the first having an ES1 in block 14, while the second has an ES2 in block 14.) The Decoder can operate in the same manner on all separations and channels associated with the image.

With reference to FIG. 19E, the Comparator Module 504 can be configured to compare each of the decoded codes (e.g., ES1 and ES2, from all separations) with any associated 1D and/or 2D barcode codes. For example, in the illustrated example, a 1D barcode is recognized and decoded by the Barcode Module 501, and the results are provided to the Comparator Module 504. The 1D barcode's code carries the same ES1 code in this example. The Comparator Module 504 notes a conflict in blocks 14 and 19, since those blocks (in various separations) include an ES2 code that is different than the 1D barcode's ES1 code. This conflict can be noted and reported to the Display Module 506.

The Display Module 506 may be configured to provide output such as a “conflict map.” The conflict map may include, e.g., a report, color information, information for spatial display, or highlights for those image areas including a conflict. Display Module 506 output can be provided to a display screen. A conflict map can be shown by boxing or highlighting the relevant image blocks. In some cases, a text overlay is provided in the blocks' relative spatial position in the imagery noting the ES2 code or other text indicating a conflict. In still other cases, the conflict boxes are shown with an opaque overlay, or in gray instead of in color. Non-conflicting blocks, e.g., those blocks corresponding to the 1D barcode's code, can be shown in original image colors, and/or with text overlays (e.g., indicating an “OK” or “√” symbol or text showing the ES1 code). The text overlays can be color-coded to indicate “all clear” (e.g., green) or “conflict” areas (e.g., red).

Now with reference to FIG. 20B, the Display Module 506 may be configured to control display of additional and/or alternative information. The information can be displayed on a display such as a computer monitor, touchscreen display, smart phone screen, TV monitor, etc. The Decoder Module 502 operates on all separations for the image blocks noted in FIG. 20A. In the illustrated example, the Decoder Module 502 decodes two different encoded signal codes, ES3 and ES4. No codes are found in blocks 1, 3, 9, 18 and 19, so these blocks are illustrated as being empty. The ES3 codes are only found in blocks 2, 4, 7 and 12, whereas the ES4 code is found in various other image areas. The Barcode Module 501 determines that the image includes a 1D barcode in block 15 carrying an ES4 code, and a QR code in block 25 carrying a similar but different code relative to the ES4.

The Comparator Module 504 is configured in this example so that conflicts between the 1D barcode (carrying an ES4) and any other codes are flagged. So a conflict is noted for block nos. 2, 4, 7, 12 and 25. This information can be provided to the Display Module 506, which can create a conflict map, e.g., an overlay of different colors (as in FIG. 20B) for the conflicting areas, perhaps relative to the original image shown in FIG. 20A. Instead of overlaying colors, the respective areas may be displayed or highlighted with a graphic box, highlighting, colored shape or outline. In some cases the empty areas (e.g., those areas including no codes) are represented on a display as a grey or grayscale. This shows that no encoded signal (or an encoded signal with a low signal strength as discussed above in Sections II and III) was found in the respective image areas. Additionally, the robustness maps discussed above can be combined with a conflict display map. This will provide a complete picture for a printer or designer, e.g., showing robustness levels (perhaps with swipe information) and any code conflicts.

Graphical text boxes (and corresponding different colors) 27 can be provided by Display Module 506, or included as part of a conflict map, for display on a display screen. For example, the UPC and ES4 codes are shown in yellow since they have matching codes, while the ES3 and the QR codes are shown in different colors since they both conflict with the 1D barcode's code and with each other.

FIG. 20B can be viewed as a conflict map or information supporting a conflict map. The map may reveal information about conflicting separations. For example, the ES3 code corresponds spatially with the castle and sundial in FIG. 20A. This may give a proofer or printer an idea of where the conflict originated. For example, the Valiant Heavy Duty Hand Soap may belong to a family of soaps, each of which includes the castle and sundial in the design. The different code (ES3) may have resulted since the castle and sundial were pulled from a different family member and used in this case. Thus, a conflict map may be used to find origins of conflicting codes.

Of course, the functionality of the various modules in FIGS. 21A and 21B can be combined into one or more modules, including one or more sub-modules. For example, the Comparator Module 504 and the Results Module 506 can be combined into a single module, as can the Barcode Module 501, Decoder Module 502 and Comparator module 504.

V. Ensure Minimal Robustness for Encoded Signals

Systems and methods to help ensure minimal robustness of an encoded signal (or embedded signal) for use on printed objects are discussed with reference to FIG. 22. An embedding and robustness system may include various different modules (also referred to as “stages” in FIG. 22).

A design file, or artwork or imagery from the file, is provided to the system. The design file may include, e.g., artwork, graphics, text, designs, images, e.g., in one or more layers, channels, or combinations. The design file format may include one or more PDF files (universal format), Adobe Illustrator (vector format) or Photoshop files (raster format), etc. We sometimes refer to the design file, or artwork or imagery from the design file, as an “Image” in this section. For example, an Image may include, e.g., Cyan (C), Magenta (M), Yellow (Y) and/or Black (K), and/or one or more Spot Color(s) (S). The Image is provided to the system for signal embedding.

Initially, before embedding begins, it may be desirable to determine whether the Image or any of its elements, backgrounds or components already includes an encoded signal. This is an initial detection process to help find errant encoded signals that might be present, e.g., from reusing or misusing elements, backgrounds or components from other design files or product family members. This check is similar to the objectives of Section IV. The System can be configured to check for the presence of an encoded signal in the Image. Stages 1, 2 and 3 operate to detect and report the presence of an encoded signal.

In Stage 1 the Image can be downsampled, e.g., from 300 dpi to 100 dpi (or to 75 dpi or 50 dpi, etc.). The downsampled Image can then be processed by a so-called Blend and Press Model (referred to as “Blend Model” in FIG. 22, and sometimes as a “Bless Module” in the following text). A Blend/Bless Module is discussed below and in our U.S. patent application Ser. No. 15/918,924, filed Mar. 12, 2018, which is hereby incorporated herein by reference in its entirety including all drawings and appendices.

The Blend model operates to predict spectral reflectance of ink overprints on various substrates, as printed using common printing technologies (e.g., offset, flexo, etc.). For example, the Image may have one or more areas where a spot color is overprinted by CMY(K) inks. As discussed above, and to detect the presence of an unknown and/or unwanted encoded signal, we assume here that the original inks at pixel or print locations may have been modified to carry an encoded signal. A spectral reflectance prediction can be made for the encoded and original overprinted areas (and areas including zero or only one ink). Full spectrum (as opposed to just CIEXYZ/Lab) is preferred to support accurate signal representation across the spectrum. For example, a final printed design has different—if not competing—goals. The printed design is preferably robust or not a low-signal gain. So, if an anticipated signal detector (e.g., a digital watermark detector) has a peak illumination at or around 660 nm, the spectral reflectance in this narrow-band is helpful to determine whether a scanner will “see” the encoded signal. Additionally, the ascetics of the final printed package can be evaluated relative to a Human Visual System (HVS) model to determine whether the print will be acceptable for designers and customers. A HVS evaluation would utilize predicted spectral reflectance for wavelengths perceptible to humans, whereas the signal detector would utilized predicted reflectance at or around 660 nm.

One embodiment of a Blend Model (or Bless Module) is discussed further with reference to FIG. 23. Bless Module operates to predict or model how various print colors will interact or “blend” with one another, e.g., when printed over (e.g., “overprint” or “layered”) various different types of substrates, in connection with encoded signals. For example, a significant number of retail packages in commerce are printed to include at least some areas using “spot colors” and/or some “process colors”. More generally, Bless Module predicts or estimates spectra reflectance characteristics of an overprint of inks, e.g., superimposed or layered process color inks and/or spot colors, on various different substrates. Because of physical differences between inks and substrates, a result of printing directly onto the substrate versus onto another ink may differ and can be considered for an encoded design file.

As a refresher, so-called spot colors may include premixed inks for use instead of or in addition to process color inks. In many print environments, each spot color ink typically uses its own printing plate on a print press. Spot colors can be used instead of or in addition to process colors for consistent color accuracy, color consistency, and colors outside of process ink gamut and for technologies which are prone to specific printing errors. A common spot color system is PANTONE (http://www.pantone.com/). The PANTONE system defines several hundred different colors/inks.

Standard process inks generally include cyan (C), magenta (M), yellow (Y), and black (K). Sometimes more than four inks are used, e.g., orange (O), violet (V), and green (G). Considering that every color used in some printing presses uses its own printing plate, it is highly impractical to print using every color in a design. Process color printing was developed, in part, to address this impracticality, since most colors can be accurately approximated with a combination of four process colors, CMYK, and optionally, OVG. To create a process color which includes multiple inks, overprinting can be used.

But let's get back to blending inks in connection with Bless Module. It can be important to accurately predict or model how various print colors will interact or “blend” with one another, e.g., when overprinted onto one another on various different types of substrates. An inaccurate prediction at the design stage can result in a design that is subpar in terms of poor encoded signal robustness (e.g., low encoded signal detection) and high signal visibility. That is, a poor ink blend prediction can result in an aesthetically ugly design that doesn't read very well.

The colors present in the encoded design file and corresponding spectra information associated with the design file colors are presented to an overprint prediction module, e.g., a Kubelka-Munk blending model (“K-M”). The Kubelka-Munk color model may provide a description of reflectance for opaque samples. The model considers, e.g., the absorption and scattering occurring in a colored sample of fixed thickness, and can be applied on a wavelength by wavelength basis throughout the visible region of the electromagnetic spectrum. The original paper was published as Kubelka, P. and Monk, F., Z tech. Physik, 12, 493 (1931), and is summarized in hunterlab. (2008). The Kubelka-Munk theory and K/S, insight on color. Application Note, Vol 18, 7. (http://www.hunterlab.com/appnotes/an07_06.pdf), which are all hereby incorporated herein by reference in their entirety. See also Dfimbeg-Malcic et al., “KUBELKA-MUNK THEORY IN DESCRIBING OPTICAL PROPERTIES OF PAPER (I),” Technical Gazette 18, 1(2011), 117-124, which is hereby incorporated herein by reference in its entirety.

We preferably use a K-M model to estimate a resulting spectral reflectance of certain ink combinations, or in other terms, estimate the reflectance of the so-called Neugebauer Primaries (“NPs”). Neugebauer's model is discussed, e.g., in Wyble et al., “A critical review of spectral models applied to binary color printing,” Color Research & Application, 25(1):4-19, 2000, which is hereby incorporated herein by reference in its entirety. Spectral reflectance of an image area can be expressed as the sum of the reflectance of each combination of ink (e.g., the Neugebauer Primaries) weighted by the relative proportion of the paper it occupies. The NPs estimated by the K-M model can be used to identify the corners or boundaries of an overprint. But further refinement is preferred.

The boundary or corner outputs (NPs) of the K-M blending model are provided to a blending model, which estimates reflectance of the rest of (or subset of) the NPs for the overprints across the visible spectrum. For example, the module can estimate reflectance values for possible combinations of these NPs. A Yule-Nielsen Modified Spectral Neugebauer (YNSN) blending model can be used for this further refinement. See, e.g., J. A. C. Yule, W. J. Nielsen, “The penetration of light into paper and its effect on halftone reproductions,” Proc. TAGA, Vol. 3, 65-76 (1951) and Hersch, et. al. “Improving the Yule-Nielsen modified spectral Neugebauer model by dot surface coverages depending on the ink superposition conditions,” Electronic Imaging Symposium, Conf. Imaging X: Processing, Hardcopy and Applications, January 05, SPIE Vol. 5667, 434-445, which are each incorporated herein by reference.

Sampling points (e.g., certain wavelengths) within the visible spectrum can be considered as well, and also dot gain. Dot gain information is provided to the YNSN blending model. (The “YN” of the YNSN model incorporates dot gain effect into the Spectral Neugebauer Model (SN part).) There are two primary components of dot gain: mechanical (the spreading of ink under pressure) and optical (light scatter within the substrate around the perimeter of a dot). We can report dot gain to the YNSN module in terms of Tone Value Increase (Tvi). From a measurement point of view, dot gain and Tvi are identical. We think that Tvi is gaining traction in the field so we use it here. The output of the YNSN blending model yields reflectance spectra estimates for ink overprinting across the visible spectrum. These outputs are for image areas such as a pixel, area or object area. In another implementation, the YNSN blending model yields reflectance spectra estimates for ink overprinting at a particular wavelength, e.g., at or around 660 nm or at 660 nm.

A percent-ink to reflectance value module (Poly model) can be optionally used to help map percent of ink changes to reflectance changes. For example, if a design only calls for an ink printed at 50%, how does this effect a reflectance prediction? This module is applied to the encoded design file to determine ink percentages within the file. These ink percentages are used to scale or adjust the predicted spectra reflectance estimates for the encoded design file.

Returning to FIG. 23, the Bless model outputs spectra reflectance information for the encoded design file, e.g., on a pixel by pixel basis (or other image area). For each pixel, an array of estimated reflectance spectrum samples (e.g., at different wavelengths) can be stored or utilized. In different implementations, this spectra reflectance information may also have or be limited to a spectral dependence limitation, e.g., containing information only at or around 660 nm, instead of providing spectral reflectance information across the visible spectrum. A Spectral proof can be generated from the spectral reflectance information. For example, a grayscale image can be produced from the spectral reflectance estimates on a per pixel basis at or around 660 nm, e.g., at 660 nm. This grayscale image would be an estimate of what a red LED POS scanner would see from an image capture of a printed version of the encoded design file. (Or an L*a*b* digital image version can be generated from a weighted sum of the estimated reflectance spectra samples per pixel across the estimated reflectance spectra information for the encoded design file.)

Returning to FIG. 22, such a “Grayscale Image (100 dpi)” is shown as output from Stage 1 (Bless Module). This Grayscale Image is provided to Stage 2 for signal detection. For example, a signal detector operates on the Grayscale Image to in search of an encoded signal. See, e.g., assignee's U.S. Pat. Nos. 9,380,186, 9,582,844 and 6,614,914, and assignee's US Published Patent Application Nos. US 2016-0275639 A1, US 2016-0198064 A1, US 2017-0024840 A1 and US 2016-0217547 A1, which are each hereby incorporated herein by reference in its entirety, for a discussion on signal detectors. An encoded signal may carry or convey a plural-bit payload, e.g., a GTIN or other identifier. Items in stores are typically assigned an identification number in a numbering scheme managed by GS1 called a Global Trade Identification Number (GTIN). The GTIN plays a vital role within store operations as it identifies products and acts as a database key to associate the product with product attributes including its name and price. For many products, the GTIN is assigned by the manufacturer of the item and encoded in the packaging, via a UPC Symbol. We prefer a signal encoding scheme that provides redundant encoding across a package, including across multiple package sides, or across a printed object like a hang tag. Digital watermarking and other signal encoding may provide redundant encoding so as to replicate a GTIN or other identifier in two-dimensional tiles across a file design.

Instead of just analyzing the Grayscale Image for payload detection, as shown at Stage 2, a signal detector may also operate on the original color Image itself, or from data representing some or all of the color Image. Analyzing this additional data may pick up other signal encoding that is not detectable from the Grayscale Image alone. For example, the signal detector may operate on individual color channel (C, M, Y and/or K), or may operate on, e.g., Luminance or “a” or “b” channel data representing the Image.

Recovered Payload Data, if any, can be provided to Stage 3 for data or payload aggregation. Stage 3 may report or provide outputs, e.g., whether an encoded signal is found, payload information (e.g., a GTIN), payload coordinates (e.g., where the payload was found spatially relative to the image), and also whether multiple payloads were found. Such reports can be used to notify a designer that he or she may have reused or misused artwork from a different design file, or from a different product family member.

The original color Image is provided to Stage 4 for signal encoding (also referred to as “embed” or “embedding”). For example, the embedding techniques described in assignee's U.S. Pat. Nos. 9,380,186, 9,582,844 and 6,614,914, and assignee's US Published Patent Application Nos. US 2016-0275639 A1, US 2016-0217547 A1, US 2016-0198064 A1, US 2017-0024840 A1 and US 2016-0217547 A1, can be used in the Stage 4 embedding. Each of the patent documents recited in this paragraph is hereby incorporated herein by reference in its entirety.

Various detailed embedding examples of protocols and processing stages of these protocols are provided in our prior work, such as our U.S. Pat. Nos. 6,614,914, 5,862,260, and 6,674,876, which are hereby incorporated by reference, and US Patent Publications 20140142958 and 20100150434, previously incorporated. More background on signaling protocols, and schemes for managing compatibility among protocols, are provided in U.S. Pat. No. 7,412,072, which is hereby incorporated by reference. Signal design may involve a balancing of required robustness, data capacity, and perceptual quality. One signaling approach, which is detailed in U.S. Pat. Nos. 6,614,914, and 5,862,260, is to map elements to pseudo-random locations within a channel defined by a domain of a host signal. See, e.g., FIG. 9 of U.S. Pat. No. 6,614,914. In particular, elements of an encoded signal can be assigned to pseudo-random embedding locations within an arrangement of sub-blocks within a block (referred to as a “tile”). The elements of this encoded signal correspond to error correction coded bits. These bits can be modulated onto a pseudo-random carrier to produce signal elements, which in turn, can be assigned to the pseudorandom embedding locations within the sub-blocks. An embedder module modulates this signal onto a host signal by increasing or decreasing host signal values at these locations for each error correction coded bit according to the values of the corresponding elements of the modulated carrier signal for that bit. (A corresponding signal decoder can estimate coded bits by accumulating evidence across the pseudo-random locations obtained after non-linear filtering a suspect host image, e.g., the Grayscale Image discussed above.)

For many applications, an image is expected to encounter distortion that will require that the image be designed to be robust to this distortion. A signaling scheme often must be robust to geometric transformation, such as translation, rotation, and scale. Projective transformations of images present challenges in applications where signals are encoded on objects and captured with a 2D image sensor, used in cameras, scanners, etc. One cannot control the perspective at which a 2D sensor array captures the signal from an object, and as such, the signaling scheme needs to handle a range of perspective distortion.

The need for synchronization can be reduced by selecting data signaling schemes that are inherently robust to distortion. For example, the modulation of the host may be conducted in a domain that is robust to geometric or temporal distortion (e.g., Fourier magnitude coefficients robust to spatial or temporal translation). While this may reduce or simplify synchronization, it can limit other capabilities of the signaling scheme, like data capacity or perceptual quality. As a result, the signaling scheme often needs to be augmented with an explicit synchronization signal or may have an implicit synchronization signal.

Examples of explicit and implicit synchronization signals are provided in our previously cited patents U.S. Pat. Nos. 6,614,914, and 5,862,260. In particular, one example of an explicit synchronization signal is a signal comprised of a set of sine waves, with pseudo-random phase, which appear as peaks in the Fourier domain of the suspect signal. See, e.g., U.S. Pat. Nos. 6,614,914, and 5,862,260, describing use of a synchronization signal in conjunction with a robust data signal. Also see U.S. Pat. No. 7,986,807, which is hereby incorporated by reference.

Our US Publication 20120078989, which is hereby incorporated by reference, provides additional methods for detecting an embedded signal with this type of structure and recovering rotation, scale and translation from these methods.

Examples of implicit synchronization signals, and their use, are provided in U.S. Pat. Nos. 6,614,914 and 5,862,260, as well as U.S. Pat. Nos. 6,625,297 and 7,072,490, which are hereby incorporated by reference.

Returning to FIG. 22, a Payload (e.g., a GTIN or other identifier) is provided to Stage 4. This Payload is used as (or to derive) the information to be carried by the encoded signal. Stage 4 includes or calls a signal encoder, which will transform the Image to carry an encoded signal, thereby enhancing the Image to include an encoded signal therein. The transformation may involve, e.g., digital watermarking or other signal encoding. Digital watermarking may introduce CMY(K) color channel modifications to carry a signal, modify spot colors to carry encoded signals, combine colors to convey a signal, substitute colors, and/or modify colors along various axes (e.g., Lab), etc., all to encode an auxiliary signal within the design file. The signal encoder may also receive printing press information associated with a target printing press. For example, a given press or press family model may be known to have a printing tolerance that varies from accepted standards. The signal encoding may be adjusted, e.g., by increasing or decreasing signal gain, to accommodate such variances.

An embedding mask can also be provided to the Stage 4 embedder. For example, a designer or artwork owner may identify areas within the Image to avoid or to heavily embed. Additional masks are discussed below. Stage 4 outputs an encoded Image (“Marked Image CMYS+S (300 dpi)”) which includes the Payload encoded therein.

The encoded Image can be provided to Stage 5, e.g., which includes visibility analysis for the encoded image. For example, the technology described in Assignee's U.S. Pat. No. 9,565,335 (“'335 patent”), which is hereby incorporated herein by reference can be used. See FIG. 24 (corresponding to FIG. 21 from the '335 patent), where a digital watermarked image is compared to a processed version of an original image to determine a visibility map. Such a map can be used as an embedding mask In particular, the visibility map can be used to weight watermark embedding of the original image, e.g., to reduce watermark strength in high visibility areas. The process starts with conversion of an original image into the so-call CIELAB space, resulting in L*, a* and b* color representations. As mentioned above, the L* coordinate represents the perceived lightness or luminance, an L* value of 0 indicates black and a value of 100 indicates white. The CIE a* coordinate position goes between “redness” (positive) and “greenness” (negative), while the CIE b* goes between “yellowness” (positive) and “blueness” (negative). The original image is digitally watermarked and then converted into the CIELAB space. For example, the watermarking for this initial process may use a uniform embedding strength across the entire image. Contrast between the original image and the marked image can be determined, and then contrast sensitivity functions (CSFs) can be applied to each of the L*, a* and b* channels. For example, the L* CSFs discussed in Daly, “Visible differences predictor: an algorithm for the assessment of image fidelity,” F. L. van Nes et al. “Spatial Modulation Transfer in the Human Eye,” J. Opt. Soc. Am., Vol. 57, Issue 3, pp. 401-406 (1967), or Johnson et al, “On Contrast Sensitivity in an Image Difference Model,” PICS 2002: Image Processing, Image Quality, Image Capture Systems Conference, Portland, Oreg., April 2002; p. 18-23 (which is herein incorporated herein in its entirety), can be used. In other cases a bandpass filter, with a drop off toward low-frequencies, can be applied to the L*. The processed or blurred L* channel (from the original image) can be used to determine visibility masking. For example, areas of high contrast, edges, features, high variance areas, can be identified for inclusion of more or less watermarking strength. Some areas (e.g., flat area, edges, etc.) can be entirely masked out to avoid watermarking all together.

One limitation of the FIG. 24 model is that it does not take into account local luminance influences for Contrast Sensitivity Functions (CSF), particularly for the a* and b* chrominance channels. With reference to FIG. 25 (corresponding to FIG. 22 in the '335 patent), we propose a color visibility model for use with a digital watermark embedder that seeks equal visibility across an image by locally varying watermarking embedding strength based on predicted visibility influenced, e.g., by local image luminance. A CSF for each color channel can be varied spatially depending on the luminance of the local image content.

The luminance content of the original image provides potential masking of changes due to watermarking in chrominance as well as luminance. For example, where a watermark signal comprises mostly high frequency components, the masking potential of the original image is greater at regions with high frequency content. We observe that most high frequency content in a typical host image is in the luminance channel. Thus, the luminance content of the host is the dominant contributor to masking potential for luminance changes and chrominance changes for high frequency components of the watermark signal.

Returning to FIG. 25, we may add several modules relative to the FIG. 24 system, e.g., “Calculate Local Luminance” and “blur SCALED CSF” modules. The FIG. 25 visibility model system uses separate CSFs for contrast variations in luminance and chrominance (red-green and blue-yellow) channels. The width, characteristics or curve of the CSF in each channel can be scaled or modified depending on the luminance of the local image content. For example, for a given pixel, local luminance in a neighborhood around the pixel can be evaluated to determine a local brightness value. The local brightness value can be used to scale or modified a CSF curve. The neighborhood may include, e.g., 4, 8 or more pixels. In some cases, the CSF is adjusted so that more blurring occurs as the luminance of the local region decreases. The error difference between the contrast of the blurred (or un-blurred) original and the blurred marked image can be measured using a color difference metric, e.g., ΔE76, ΔE94 or ΔE2000.

With reference to FIG. 26 (corresponding to FIG. 23A in the '335 patent), one objective may include embedding digital watermarking into images with equal visibility. That is, the image includes watermarking embedded therein at different signal strength values to achieve uniform or equal visibility. During the embedding stage, the visibility model can predict the visibility of the watermark signal and then adjust the embedding strength. The result will be an embedded image with a uniform watermark signal visibility, with the embedding strength varying locally across the image depending on characteristics of the cover image's content. For example, a visibility map generated from the FIG. 24 system is used to reshape (e.g., locally scale according to an error map and/or mask embedding or avoidance areas according to a visibility map) a watermark signal.

Stage 5 (FIG. 22) can output a reshaped embedded Image which may be a more equally visibility embedded (EVE) image compared to the embedded image. The reshaping can involve, e.g., variable local encoded signal weightings across the image. In other implementations, Stage 5 produces a mask (FIG. 26) for watermark scaling that is used as the Stage 4 mask. In still other implementations, a visibility map (e.g., FIG. 25, “Visibility Map”) is produced and provided to Stage 4 and used as a mask. Of course, Stage 5 may be incorporated into Stage 4 itself.

Stage 6 can be a duplicate stage (or the same operation) as Stage 1. In practice, the system may call Stage 1 again (e.g., in a software embodiment), or may implement the dual stage (e.g., both Stages 1 and 6 in hardware). Here, though, the “EVE Shaped Image CMYK+S (300 dpi)” image output from Stage 5 is the input to Stage 6. A Bless Module operates on the input image to produce spectral reflectance information corresponding to the encoded/marked input image. For example, a Grayscale image corresponding to spectral reflectance information at or around 660 nm can be generated and provided to Stage 7.

Stage 7 determines robustness measures from the marked Grayscale Image for the marked image. (The robustness, grading and aggregation technology from our U.S. patent application Ser. No. 15/918,924, filed Mar. 12, 2018, and U.S. Provisional Patent Application No. 62/682,750, filed Jun. 8, 2018, can be used in Stage 7 as well. Each of the above patent documents is hereby incorporated herein by reference in its entirety.) In one implementation, the metrics discussed above in Section II are used to evaluate robustness for the marked Grayscale Image. And, the marked Grayscale Image can be “attacked,” or transformed e.g., to predict how the digital image, once embedded with encoded signals and before printing, will be detectable after printing.

The marked Grayscale Image can be blurred, transformed with noise, and shaped to mimic how a device will perceive an image scanned from a curved or trapezoidal container. Shaping helps adapt the encoded Grayscale Image according to anticipated printed object geometry. This simulates an amount of encoded signal degradation introduced from wrapping following a package form factor. For example, if the encoded Image is destined to be printed on a can of soup, the contour of a cylinder can be applied to the encoded Grayscale Image. For example, the encoded Grayscale Image can be warped to simulate a cylinder view. In one implementation, and for a cylinder, we divide an image into vertical stripes, e.g., 4 quadrants. The center areas of these quadrants are viewed as flat, e.g., they are not warped, or warped slightly. The outer and border areas are warped with a transformation to mimic an image capture of a curved or cylindrical surface. This will give a more realistic estimation of signal strength as if the encoded design file were printed and then applied to a curved (or other geometry) surface. If the encoded Image is intended for a flat sheet or for a plurality of flat sheets (e.g., 6 images for a 6-sided package), then the encoded Grayscale Image can be unaltered without warping. Geometric adaptation transforms the encoded Grayscale Image to represent an optical capture of a printed encoded Image, on a particular surface, e.g., flat (full sheet or surface), cylindrical package (with vertical profile), or cylindrical with tapering (like yogurt cup), etc. Additionally, Stage 7 may estimate 3D geometry, orientation, and trajectory of a printed package or object for use in estimating a captured image. This includes a visual representation of the package in 3D as well as the orientation and trajectory which might describe interaction with the printed package or object, e.g., front of store checker motion and/or customer interaction with the printed package or object on the shelf or rack.

Other capture device (e.g., Point of Sale scanner) specific features can also be used to attack the marked Grayscale Image. The capture device is defined as any of the devices used to capture data or images of printed samples. The device parameters may include geometric, spatial, and spectral properties defined by a device form factor, modes of operation, and sensors. Examples of capture devices include RGB scanners, red LED POS scanners, mobile and other camera systems, and scanning or manual spectrophotometers and densitometers.

Stage 7 outputs Robustness Data. For example, the data may include the LGS (“linear Grid Strength”) and MS metrics, per image area, for the attacked marked Grayscale Image. Or the Robustness Data may include a heatmap of the digital image.

After upsampling (Stage 8), Stage 9 aggregates robustness information for the marked Grayscale Image, and generates and/or outputs robustness maps for use, e.g., by Stage 4. In one implementation, the aggregation described in Assignee's U.S. Provisional Patent Application No. 62/682,750, filed Jun. 8, 2018, and which is attached as Appendix A, and which is hereby incorporated herein by reference in its entirety, is used to provide aggregation for the system. In another implementation, the original color Image can be embedded again to achieve a predetermined robustness metric (e.g., thresholds based on the LGS and MS metrics). In other cases, the Marked Image is adjusted according to robustness information provided by Stage 9. For example, there may be areas within the Marked Image that do not initially met a predetermined or user provided robustness standard. Encoded signal gain can be increased, or a different type of encoding can be tried altogether, for these areas. E.g., a so-called Sparse watermark signal (see, e.g., US 2017-0024840 A1, which is hereby incorporated herein by reference) can be tried in an image area, instead of a Spot color encoding or CMY signal encoding (for both, see, e.g., U.S. Pat. No. 9,667,829, which is hereby incorporated herein by reference in its entirety), or other signal encoding. The Marked Image (or original Image) can be adjusted multiple times by Stages 4-7, until a desired, minimal encoding across the image is achieved. A controller (e.g., one or more programmed multi-core processors, GPU(s), ASIC(s), circuitry, specifically configured software modules, etc.) can be configured to control re-embedding of the marked Image until a threshold robustness level is reached.

The Stage 9 output can be in any format that is derived from a signal robustness metric. For example, it can be an image of the same size (width and height) as the input grey scale image, with a heat map indicating the signal strength distribution. The heat map can be alternatively applied to a constructed Lab image (or other color space), the construction utilizing the estimated reflectance spectra from the Bless model. Or the output can be a single value such as a probability measure based on the heat map or other computations, or an array of pixels or image areas with an associated strength metric or value. See, e.g., the above sections II-IV for additional details.

Stage 9 may also output a robustness score, e.g., based on a swipe metric (discussed above in Section II) or an average for an image. For example, Grade Model 204 shown in FIG. 4 of U.S. patent application Ser. No. 15/918,924, hereby incorporated herein by reference in its entirety, may combine information from signal prediction and package geometry into a single grade per package side for an ideal capture device. Additionally, this model may utilize the so-called swipe information discussed above in Section II. In our preferred case, we use an “aggregation” approach as discussed in Appendix A (i.e., U.S. Provisional 62/682,750) to yield a score or grade for robustness or signal detection, such a transform that converts an array of signal strength values into a single value. The output of the aggregation can be a “grade” or other robustness measure In this case, the “grade” or measure is independent of device and user behavior. In other cases, a “grade” or measure can be assigned according to a signal robustness for a given geometry and/or capture device. Moreover, Image may be part of a plurality of images (e.g., 6 images for all 6 sides of a package). The 6 images can be evaluated for robustness, with robustness information aggregated, to achieve an overall package score, measure or grade, e.g., as discussed in the Ser. No. 15/918,924 application. Embedding may continue across the 6 images (or a subset of such) until the package score meet a minimal robustness threshold.

The system illustrated in FIG. 22 shows vertical “Application,” “Core” and “Application” sections. It should be appreciated, however, that these sections can be combined or distributed across networks, devices, applications, processors, servers, and modules. The “Application” sections can be integrated into a plug-in or SDK, with the “Core” being similarly integrated or constructed as, e.g., a library of functions hosted by a web service or integrated into image and graphic editing software (e.g., Illustrator or Photoshop or products by Esko, e.g., “PackEdge”). The “parameters” and “configuration” layers may be user selected or provided to establish information about certain colors, embedding strengths, minimal acceptable robustness, etc. The “Spot Color Database” may provide color spectral information usable by, e.g., the Bless module.

FIG. 27 is a diagram showing another embedding system, including some of the modules from the FIG. 22 system. Stages 1-5 operate as discussed above relative to FIG. 22. Stages 6-9 from FIG. 22 are not included in the FIG. 27 implementation. A few differences, e.g., payload detection information can be used to help inform the downsampling. For example, a watermark scale or bump size can be used to help select or determine a downsampling factor.

FIG. 27 does include a Stage 10, including a “Smart Mask” module and a “Min Dot” module. The Smart Mask module retains, eliminates and/or reduces encoded signal strengthen in terms of Image luminance (or perceived brightness) changes in Image areas, where luminance nears its top rail. For example, luminance can be viewed as being valued between 100% (max) and 0% (min), with 100% being the top rail.

Recall from the above discussion of FIG. 14 that we describe embedding a signal into units of a host signal, e.g., where a unit may include a spatial area of a two-dimensional tile within a host signal. The unit may also comprise a so-called “waxel,” which is a spatial location within an Image at which a positive or negative adjustment can be made to convey an element of an encoded signal message and/or reference signal. A waxel may represent, e.g., 2×2 pixels, 4×4 pixels, etc. The element of the encoded signal message and/or reference signal may include a tweak to the existing luminance or chrominance value, e.g., a positive or negative value adjustment change, e.g., a 4-20% positive or negative change to a luminance value per pixel or per area within the waxel.

The Smart Mask preferably operates at the waxel level, however, it may operate on areas larger or smaller than a waxel. The Smart Mask creates a smooth taper for an encoding positive or negative adjustment (or tweak) as a waxel's luminance (or, if applied to color, its color value) approaches its top rail. With reference to FIG. 28, a luminance tweak value is shown as a function of unmarked (or unencoded) luminance values. In the area between the graph's origin and A (dotted line), the tweak value remains at 100%. We can refer to A as a clip anchor, or the beginning of the tweak reduction. The tweak value decreases as the unmarked image Luminance increases from A to C. C represents the point at which the signal tweak value is reduced to zero (e.g., a “clip point”). The area between C and railed luminance comprises a full clipping, or zero tweak value. The tweak value can be recovered by subtracting Luminance of an encoded area from Luminance of a corresponding unmarked or unencoded area (LM-LU in the graph). Typical luminance values for A are between or include 60-85% luminance, and typical values for C are between or include 75-90% luminance. The slope between A and C can be linear (as shown), or can be non-linear, e.g., taking into consideration the human visual system (HVS) curve.

A corresponding Smart Mask Algorithm is described with reference to FIG. 29. For a particular waxel, determine whether a waxel mask is appropriate (e.g., whether to apply the Smart Mask). If luminance of any pixel value (before receiving encoding, LU) within the waxel is LU<=A, then the waxel mask is not applied to any pixel value within the waxel (or alternatively, just not applied to that particular pixel). So the Luminance output for that waxel or pixel is equal to the encoded luminance value. If however, LU>A for all pixels values within the waxel, then a waxel mask is applied to the waxel. The mask can be determined as:

f_(n)=max (0, (C−LU)/(C−A)), where the max function returns the maximum of the two values, 0 or (C−LU)/(C−A), for each of n pixels, n is pixel index number, satisfying LU>A. The G term is a compression factor representation for a particular pixel, pixel 1 to n. A waxel compression or scaling factor F is then determined from:

F=min (f₁, f_(n)), where the min function returns the minimum f pixel compression value for all pixels evaluated in the waxel from pixel number 1 to n. That minimum f pixel compression value is then applied as the waxel compression value, F, for all pixels in the waxel according to LO=(LM−LU)*F or LO=tweak value*F. (Of course, while we prefer to apply a compression factor across all pixels in a waxel, we can alternatively smooth or compress on a pixel-by-pixel basis.)

Returning to FIG. 27, a Min Dot module can be used to help determine whether to apply a tweak value, and if applying, an amount of tweak value, based on a Min Dot value associated with a printing press. Min Dot is the short name for “minimum dot”. This module prevents signal encoding in Images in a printing environment below a predetermined Min Dot % value. Min Dot can be represented by a percentage (%) value that maps to a pixel value between 0 and 255. This technology can be helpful when encoding signal for press environments with a known or predetermined Min Dot %.

A Min Dot tweak smoothing algorithm is described with reference to FIG. 30.

(1) If one or more inks in a color design are in [0%, min-dot-anchor A₁%), apply min dot algorithm to clip, e.g., scale by zero (0) or compress tweaks:

For each ink:

-   -   (1a) if unmarked ink is in [min-dot-anchor A₁%, 100%], leave         tweak as is.     -   (1b) If unmarked ink is in (min-dot C₁%, min-dot-anchor A₁%),         compress the tweak according to a scaling factor. For example,         the factor can be linear (as shown in FIG. 30, or non-linear).     -   (1c) If unmarked ink is in [0%, min-dot C₁%], fully clip the         tweak.

(2) Use an average compression factor across all inks where ink is present for compressing or clipping the current pixel to reduce pixel color shifts.

For each waxel:

(3) Track the maximum compression factor calculated for each pixel in step (2) and use that factor when compressing pixels in the current waxel to reduce dot and/or hole poking. Hole poking here references to creating holes in a background or overprint, the holes creating a pattern of spectral reflection differences between the background that can convey a signal. See, e.g., U.S. Pat. No. 9,635,378, which is hereby incorporated herein by reference.

For each pixel:

(4) In single ink channel open areas (e.g., no ink areas), clip all tweaks where unmarked ink is 0%. That is, don't add ink in these areas.

VI. Operating Environments

The components and operations of the various described embodiments can be implemented in modules. Notwithstanding any specific discussion of the embodiments set forth herein, the term “module” may refer to software, firmware and/or circuitry configured to perform any of the methods, processes, functions or operations described herein. Software may be embodied as a software package, code, instructions, instruction sets or data recorded on non-transitory computer readable storage mediums. Software instructions for implementing the detailed functionality can be authored by artisans without undue experimentation from the descriptions provided herein, e.g., written in C, C++, MatLab, Visual Basic, Java, Python, Tcl, Perl, Scheme, Ruby, and assembled in executable binary files, etc., in conjunction with associated data. Firmware may be embodied as code, instructions or instruction sets or data that are hard-coded (e.g., nonvolatile) in memory devices. As used herein, the term “circuitry” may include, for example, singly or in any combination, hardwired circuitry, programmable circuitry such as one or more computer processors comprising one or more individual instruction processing cores, parallel processors, state machine circuitry, or firmware that stores instructions executed by programmable circuitry.

Applicant's work also includes taking the scientific principles and natural laws on which the present technology rests, and tying them down in particularly defined implementations. One such implementation is electronic circuitry that has been custom-designed and manufactured to perform some or all of the component acts, as an application specific integrated circuit (ASIC).

To realize such an implementation, some or all of the technology is first implemented using a general purpose computer, using software such as MatLab (from Mathworks, Inc.). A tool such as HDLCoder (also available from MathWorks) is next employed to convert the MatLab model to VHDL (an IEEE standard, and doubtless the most common hardware design language). The VHDL output is then applied to a hardware synthesis program, such as Design Compiler by Synopsis, HDL Designer by Mentor Graphics, or Encounter RTL Compiler by Cadence Design Systems. The hardware synthesis program provides output data specifying a particular array of electronic logic gates that will realize the technology in hardware form, as a special-purpose machine dedicated to such purpose. This output data is then provided to a semiconductor fabrication contractor, which uses it to produce the customized silicon part. (Suitable contractors include TSMC, Global Foundries, and ON Semiconductors.)

Another specific implementation of the present disclosure includes barcode and/or encoded signal detection operating on a specifically configured smartphone (e.g., iPhone 6 or Android device) or other mobile device, such phone or device. The smartphone or mobile device may be configured and controlled by software (e.g., an App or operating system) resident on the smartphone device. The resident software may include, e.g., a barcode decoder, digital watermark detector and detectability measure generator module.

For the sake of further illustration, FIG. 8 is a diagram of an electronic device (e.g., a computer, smartphone, mobile device, tablet, laptop, or other electronic device) in which the components of the above encoder, decoder, and various Module embodiments may be implemented. It is not intended to be limiting, as the embodiments may be implemented in other device architectures or electronic circuitry.

Referring to FIG. 8, a system for an electronic device includes bus 100, to which many devices, modules, etc., (each of which may be generically referred as a “component”) are communicatively coupled. The bus 100 may combine the functionality of a direct memory access (DMA) bus and a programmed input/output (PIO) bus. In other words, the bus 100 may facilitate both DMA transfers and direct CPU read and write instructions. In one embodiment, the bus 100 is one of the Advanced Microcontroller Bus Architecture (AMBA) compliant data buses. Although FIG. 8 illustrates an embodiment in which all components are communicatively coupled to the bus 100, it will be appreciated that one or more sub-sets of the components may be communicatively coupled to a separate bus in any suitable or beneficial manner, and that any component may be communicatively coupled to two or more buses in any suitable or beneficial manner. Although not illustrated, the electronic device can optionally include one or more bus controllers (e.g., a DMA controller, an I2C bus controller, or the like or any combination thereof), through which data can be routed between certain of the components.

The electronic device also includes a CPU 102. The CPU 102 may be any microprocessor, multi-core microprocessor, parallel processors, mobile application processor, etc., known in the art (e.g., a Reduced Instruction Set Computer (RISC) from ARM Limited, the Krait CPU product-family, any X86-based microprocessor available from the Intel Corporation including those in the Pentium, Xeon, Itanium, Celeron, Atom, Core i-series product families, etc.). Another CPU example is an Apple A8 or A7. The A8 is built on a 64-bit architecture, includes a motion co-processor and is manufactured on a 20 nm process. The CPU 102 runs an operating system of the electronic device, runs application programs (e.g., mobile apps such as those available through application distribution platforms such as the Apple App Store, Google Play, etc., or custom designed to include watermark detection and object authentication) and, optionally, manages the various functions of the electronic device. The CPU 102 may include or be coupled to a read-only memory (ROM) (not shown), which may hold an operating system (e.g., a “high-level” operating system, a “real-time” operating system, a mobile operating system, or the like or any combination thereof) or other device firmware that runs on the electronic device. Watermark detection capabilities can be integrated into the operating system itself.

The electronic device may also include a volatile memory 104 electrically coupled to bus 100. The volatile memory 104 may include, for example, any type of random access memory (RAM). Although not shown, the electronic device may further include a memory controller that controls the flow of data to and from the volatile memory 104.

The electronic device may also include a storage memory 106 connected to the bus. The storage memory 106 typically includes one or more non-volatile semiconductor memory devices such as ROM, EPROM and EEPROM, NOR or NAND flash memory, or the like or any combination thereof, and may also include any kind of electronic storage device, such as, for example, magnetic or optical disks. In embodiments of the present invention, the storage memory 106 is used to store one or more items of software. Software can include system software, application software, middleware (e.g., Data Distribution Service (DDS) for Real Time Systems, MER, etc.), one or more computer files (e.g., one or more data files, configuration files, library files, archive files, etc.), one or more software components, or the like or any stack or other combination thereof. Examples of system software include operating systems (e.g., including one or more high-level operating systems, real-time operating systems, mobile operating systems, or the like or any combination thereof), one or more kernels, one or more device drivers, firmware, one or more utility programs (e.g., that help to analyze, configure, optimize, maintain, etc., one or more components of the electronic device), and the like.

Application software typically includes any application program that helps users solve problems, perform tasks, render media content, retrieve (or access, present, traverse, query, create, organize, etc.) information or information resources on a network (e.g., the World Wide Web), a web server, a file system, a database, etc. Examples of software components include device drivers, software CODECs, message queues or mailboxes, databases, etc. A software component can also include any other data or parameter to be provided to application software, a web application, or the like or any combination thereof. Examples of data files include image files, text files, audio files, video files, haptic signature files, and the like.

Also connected to the bus 100 is a user interface module 108. The user interface module 108 is configured to facilitate user control of the electronic device. Thus the user interface module 108 may be communicatively coupled to one or more user input devices 110. A user input device 110 can, for example, include a button, knob, touch screen, trackball, mouse, microphone (e.g., an electret microphone, a MEMS microphone, or the like or any combination thereof), an IR or ultrasound-emitting stylus, an ultrasound emitter (e.g., to detect user gestures, etc.), one or more structured light emitters (e.g., to project structured IR light to detect user gestures, etc.), one or more ultrasonic transducers, or the like or any combination thereof.

The user interface module 108 may also be configured to indicate, to the user, the effect of the user's control of the electronic device, or any other information related to an operation being performed by the electronic device or function otherwise supported by the electronic device. Thus the user interface module 108 may also be communicatively coupled to one or more user output devices 112. A user output device 112 can, for example, include a display (e.g., a liquid crystal display (LCD), a light emitting diode (LED) display, an active-matrix organic light-emitting diode (AMOLED) display, an e-ink display, etc.), a light, an illumination source such as a flash or torch, a buzzer, a haptic actuator, a loud speaker, or the like or any combination thereof. In the case of an iPhone 6, the flash includes a True Tone flash including a dual-color or dual-temperature flash that has each color firing at varying intensities based on a scene to make sure colors and skin tone stay true.

Generally, the user input devices 110 and user output devices 112 are an integral part of the electronic device; however, in alternate embodiments, any user input device 110 (e.g., a microphone, etc.) or user output device 112 (e.g., a loud speaker, haptic actuator, light, display, or printer) may be a physically separate device that is communicatively coupled to the electronic device (e.g., via a communications module 114). A printer encompasses many different devices for applying our encoded signals to objects, such as 2D and 3D printers, etching, engraving, flexo-printing, offset printing, embossing, laser marking, etc. The printer may also include a digital press such as HP's indigo press. An encoded object may include, e.g., a consumer packaged product, a label, a sticker, a logo, a driver's license, a passport or other identification document, etc.

Although the user interface module 108 is illustrated as an individual component, it will be appreciated that the user interface module 108 (or portions thereof) may be functionally integrated into one or more other components of the electronic device (e.g., the CPU 102, the sensor interface module 130, etc.).

Also connected to the bus 100 is an image signal processor 116 and a graphics processing unit (GPU) 118. The image signal processor (ISP) 116 is configured to process imagery (including still-frame imagery, video imagery, or the like or any combination thereof) captured by one or more cameras 120, or by any other image sensors, thereby generating image data. General functions typically performed by the ISP 116 can include Bayer transformation, demosaicing, noise reduction, image sharpening, filtering, or the like or any combination thereof. The GPU 118 can be configured to process the image data generated by the ISP 116, thereby generating processed image data. General functions typically performed by the GPU 118 include compressing image data (e.g., into a JPEG format, an MPEG format, or the like or any combination thereof), creating lighting effects, rendering 3D graphics, texture mapping, calculating geometric transformations (e.g., rotation, translation, etc.) into different coordinate systems, etc. and send the compressed video data to other components of the electronic device (e.g., the volatile memory 104) via bus 100. The GPU 118 may also be configured to perform one or more video decompression or decoding processes. Image data generated by the ISP 116 or processed image data generated by the GPU 118 may be accessed by the user interface module 108, where it is converted into one or more suitable signals that may be sent to a user output device 112 such as a display, printer or speaker. GPU 118 may also be configured to serve one or more functions of a watermark detector. In some cases GPU 118 searches for a watermark orientation component, while payload resolution is performed by the CPU 102.

Also coupled the bus 100 is an audio I/O module 122, which is configured to encode, decode and route data to and from one or more microphone(s) 124 (any of which may be considered a user input device 110) and loud speaker(s) 126 (any of which may be considered a user output device 110). For example, sound can be present within an ambient, aural environment (e.g., as one or more propagating sound waves) surrounding the electronic device. A sample of such ambient sound can be obtained by sensing the propagating sound wave(s) using one or more microphones 124, and the microphone(s) 124 then convert the sensed sound into one or more corresponding analog audio signals (typically, electrical signals), thereby capturing the sensed sound. The signal(s) generated by the microphone(s) 124 can then be processed by the audio I/O module 122 (e.g., to convert the analog audio signals into digital audio signals) and thereafter output the resultant digital audio signals (e.g., to an audio digital signal processor (DSP) such as audio DSP 128, to another module such as a song recognition module, a speech recognition module, a voice recognition module, etc., to the volatile memory 104, the storage memory 106, or the like or any combination thereof). The audio I/O module 122 can also receive digital audio signals from the audio DSP 128, convert each received digital audio signal into one or more corresponding analog audio signals and send the analog audio signals to one or more loudspeakers 126. In one embodiment, the audio I/O module 122 includes two communication channels (e.g., so that the audio I/O module 122 can transmit generated audio data and receive audio data simultaneously).

The audio DSP 128 performs various processing of digital audio signals generated by the audio I/O module 122, such as compression, decompression, equalization, mixing of audio from different sources, etc., and thereafter output the processed digital audio signals (e.g., to the audio I/O module 122, to another module such as a song recognition module, a speech recognition module, a voice recognition module, etc., to the volatile memory 104, the storage memory 106, or the like or any combination thereof). Generally, the audio DSP 128 may include one or more microprocessors, digital signal processors or other microcontrollers, programmable logic devices, or the like or any combination thereof. The audio DSP 128 may also optionally include cache or other local memory device (e.g., volatile memory, non-volatile memory or a combination thereof), DMA channels, one or more input buffers, one or more output buffers, and any other component facilitating the functions it supports (e.g., as described below). In one embodiment, the audio DSP 128 includes a core processor (e.g., an ARM® AudioDE™ processor, a Hexagon processor (e.g., QDSP6V5A)), as well as a data memory, program memory, DMA channels, one or more input buffers, one or more output buffers, etc. Although the audio I/O module 122 and the audio DSP 128 are illustrated as separate components, it will be appreciated that the audio I/O module 122 and the audio DSP 128 can be functionally integrated together. Further, it will be appreciated that the audio DSP 128 and other components such as the user interface module 108 may be (at least partially) functionally integrated together.

The aforementioned communications module 114 includes circuitry, antennas, sensors, and any other suitable or desired technology that facilitates transmitting or receiving data (e.g., within a network) through one or more wired links (e.g., via Ethernet, USB, FireWire, etc.), or one or more wireless links (e.g., configured according to any standard or otherwise desired or suitable wireless protocols or techniques such as Bluetooth, Bluetooth Low Energy, WiFi, WiMAX, GSM, CDMA, EDGE, cellular 3G or LTE, Li-Fi (e.g., for IR- or visible-light communication), sonic or ultrasonic communication, etc.), or the like or any combination thereof. In one embodiment, the communications module 114 may include one or more microprocessors, digital signal processors or other microcontrollers, programmable logic devices, or the like or any combination thereof. Optionally, the communications module 114 includes cache or other local memory device (e.g., volatile memory, non-volatile memory or a combination thereof), DMA channels, one or more input buffers, one or more output buffers, or the like or any combination thereof. In one embodiment, the communications module 114 includes a baseband processor (e.g., that performs signal processing and implements real-time radio transmission operations for the electronic device).

Also connected to the bus 100 is a sensor interface module 130 communicatively coupled to one or more sensor(s) 132. Sensor 132 can, for example, include an accelerometer (e.g., for sensing acceleration, orientation, vibration, etc.), a magnetometer (e.g., for sensing the direction of a magnetic field), a gyroscope (e.g., for tracking rotation, orientation, or twist), a barometer (e.g., for sensing air pressure, from which relative elevation can be determined), a wind meter, a moisture sensor, an ambient light sensor, an IR or UV sensor or other photodetector, a pressure sensor, a temperature sensor, an acoustic vector sensor (e.g., for sensing particle velocity), a galvanic skin response (GSR) sensor, an ultrasonic sensor, a location sensor (e.g., a GPS receiver module, etc.), a gas or other chemical sensor, or the like or any combination thereof. Although separately illustrated in FIG. 8, any camera 120 or microphone 124 can also be considered a sensor 132. Generally, a sensor 132 generates one or more signals (typically, electrical signals) in the presence of some sort of stimulus (e.g., light, sound, moisture, gravitational field, magnetic field, electric field, etc.), in response to a change in applied stimulus, or the like or any combination thereof. In one embodiment, all sensors 132 coupled to the sensor interface module 130 are an integral part of the electronic device; however, in alternate embodiments, one or more of the sensors may be physically separate devices communicatively coupled to the electronic device (e.g., via the communications module 114). To the extent that any sensor 132 can function to sense user input, then such sensor 132 can also be considered a user input device 110. The sensor interface module 130 is configured to activate, deactivate or otherwise control an operation (e.g., sampling rate, sampling range, etc.) of one or more sensors 132 (e.g., in accordance with instructions stored internally, or externally in volatile memory 104 or storage memory 106, ROM, etc., in accordance with commands issued by one or more components such as the CPU 102, the user interface module 108, the audio DSP 128, the cue detection module 134, or the like or any combination thereof). In one embodiment, sensor interface module 130 can encode, decode, sample, filter or otherwise process signals generated by one or more of the sensors 132. In one example, the sensor interface module 130 can integrate signals generated by multiple sensors 132 and optionally process the integrated signal(s). Signals can be routed from the sensor interface module 130 to one or more of the aforementioned components of the electronic device (e.g., via the bus 100). In another embodiment, however, any signal generated by a sensor 132 can be routed (e.g., to the CPU 102), the before being processed.

Generally, the sensor interface module 130 may include one or more microprocessors, digital signal processors or other microcontrollers, programmable logic devices, or the like or any combination thereof. The sensor interface module 130 may also optionally include cache or other local memory device (e.g., volatile memory, non-volatile memory or a combination thereof), DMA channels, one or more input buffers, one or more output buffers, and any other component facilitating the functions it supports (e.g., as described above). In one embodiment, the sensor interface module 130 may be provided as the “Sensor Core” (Sensors Processor Subsystem (SPS)) from Qualcomm, the “frizz” from Megachips, or the like or any combination thereof. Although the sensor interface module 130 is illustrated as an individual component, it will be appreciated that the sensor interface module 130 (or portions thereof) may be functionally integrated into one or more other components (e.g., the CPU 102, the communications module 114, the audio I/O module 122, the audio DSP 128, the cue detection module 134, or the like or any combination thereof).

CONCLUDING REMARKS

Having described and illustrated the principles of the technology with reference to specific implementations, it will be recognized that the technology can be implemented in many other, different, forms. To provide a comprehensive disclosure without unduly lengthening the specification, applicant hereby incorporates by reference each of the above referenced patent documents in its entirety. Such documents are incorporated in their entireties, including all drawings and appendices, even if cited above in connection with specific of their teachings. These documents disclose technologies and teachings that can be incorporated into the arrangements detailed, and into which the technologies and teachings detailed herein can be incorporated.

The particular combinations of elements and features in the above-detailed embodiments are exemplary only; the interchanging and substitution of these teachings with other teachings in this and the incorporated-by-reference patents are also contemplated. Many combinations will be evident from the above disclosure. 

What is claimed is:
 1. A system comprising: an input to receive a design file, the design file comprising an image including an area of layered colors, the image comprising luminance or chrominance values; one or more multi-core processors and a memory storing instructions that, when the instructions are executed by the one or more multi-core processors, cause the one or more multi-core processors to function as: a blend module for operating on the design file, the blend module generating reflectance spectra estimates for the image including the area of layered colors, the reflectance spectra estimates provided on a per pixel basis for the design file; a signal detector to determine whether the design file includes a signal encoded therein; a signal encoder for encoding a signal in the image to yield an encoded image, in which the signal encoder encodes the image only when the signal detector finds no signal encoded within the design file, the signal encoder transforming the image by introducing positive or negative adjustments in units of the image, with each unit representing a plurality of pixels; a visibility module for generating visibility information associated with the encoded image, and for reducing visibility of the encoded signal in at least one area of the image; a mask module for evaluating the image on a unit level to determine whether the luminance or chrominance values, prior to encoding, falls within a predetermined luminance or chrominance range, and for compressing or eliminating the positive or negative adjustments when the luminance or chrominance values fall within the predetermined luminance or chrominance range, the mask module producing a masked, encoded image.
 2. The system of claim 1 in which the signal encoder comprises a digital watermark encoder.
 3. The system of claim 1 in which the input comprises memory or a communications bus connecting the signal encoder with one or more modules of the system.
 4. The system of claim 1 in which the signal detector operates in three stages, in which two of the stages comprise i) a blend module for operating on the image, the blend module generating reflectance spectra estimates for the image including the area of layered colors, the reflectance spectra estimates provided on a per pixel basis for the image, and ii) a payload detection module for decoding one or more payloads from the any encoded signals.
 5. The system of claim 4 in which a third stage (iii) comprises a payload aggregation module for aggregating payloads decoded from the any encoded signal.
 6. The system of claim 1 in which the mask module operates by: i) determining whether luminance of a pixel value in a unit of the image prior to encoding (“LU”) comprises LU<=A, where A comprises a first predetermined luminance percentage value, and if true then a scaling factor, F, is not applied to a pixel value; ii) determining whether LU>A for remaining n pixels within the unit, where n is a pixel number, and then if true, applying a scaling factor, F, to a positive or negative adjustment according to: fn=max (0, (C−LU)/(C−A)), where the max function returns the maximum of the two values, 0 or (C−LU)/(C−A), for each of the n pixels satisfying LU>A, where fn comprises a pixel scaling factor for a particular pixel, pixel 1 to n, and C comprises a second predetermined luminance percentage value that is greater than the first predetermined pixel vlaue A; F=min (f1, fn), where the min function returns the minimum f pixel scaling value for all pixels evaluated in the unit from pixel number 1 to n.
 7. The system of claim 1 in which the mask module operates by: scaling positive or negative adjustments on a per unit basis when luminance values associated with the image prior to encoding fall within a first luminance value range; eliminating positive or negative adjustments on a per unit basis when luminance values associated with the image prior to encoding fall with a second luminance value range, the second luminance value range comprising luminance values above the first luminance value range up to 100% luminance.
 8. The system of claim 1, in which the masking module alters positive or negative adjustments depending on value ranges of the image prior to receiving encoding, the value ranges comprising: Range i: between 0% luminance to a first predetermined luminance percentage, A %; Range ii: between A % to a second predetermined luminance percentage, C %; and Range iii: between C % to 100% luminance; in which the positive or negative adjustments are not altered when luminance of the image prior to encoding (LU) falls within Range i, the positive or negative adjustments are reduced by a scaling factor when luminance of the image prior to encoding (LU) falls within Range ii, and the positive or negative adjustments are eliminated when luminance of the image prior to encoding (LU) falls within Range iii.
 9. An image processing method comprising: receiving a design file, the design file comprising an image including an area of layered colors, the image comprising luminance or chrominance values; generating reflectance spectra estimates for the image including the area of layered colors, the reflectance spectra estimates provided on a per pixel basis for the design file; detecting whether the design file includes a signal encoded therein; transforming the image by encoding a signal therein to yield an encoded image, in which said transforming encodes the image only when no signal is encoded within the design file, said transforming transforms the image by introducing positive or negative adjustments in units of the image, with each unit representing a plurality of pixels; generating visibility information associated with the encoded image, and reducing visibility of the encoded signal in at least one area of the encoded image; evaluating the image on a unit level to determine whether corresponding luminance or chrominance values, prior to encoding, fall within a predetermined luminance or chrominance range, and compressing or eliminating the positive or negative adjustments when the luminance or chrominance values fall within the predetermined luminance or chrominance range.
 10. The image processing method of claim 9 in which said transforming utilizes a digital watermark encoder.
 11. The image processing method of claim 9 in which the detecting comprises three stages, in which two of the stages comprise i) generating reflectance spectra estimates for the image including the area of layered colors, the reflectance spectra estimates provided on a per pixel basis for the image, and ii) decoding one or more payloads from the any encoded signals.
 12. The image processing method of claim 11 in which a third stage (iii) comprises aggregating payloads decoded from the any encoded signals.
 13. The image processing method of claim 9 in which the evaluating comprises: i) determining whether luminance of a pixel value in a unit of the image prior to encoding (“LU”) comprises LU<=A, where A comprises a first predetermined luminance percentage value, and, if true, not apply a scaling factor, F, a corresponding pixel value; ii) determining whether LU>A for remaining n pixels within the unit, where n is a number of remaining pixels, and then if true, applying a scaling factor, F, to a positive or negative adjustment according to: fn=max (0, (C−LU)/(C−A)), where the max function returns the maximum of the two values, 0 or (C−LU)/(C−A), for each of the n pixels satisfying LU>A, where fn comprises a pixel scaling factor for a particular pixel, pixel 1 to n, and C comprises a second predetermined luminance percentage value that is greater than the first predetermined pixel value A; where F=min (f1, fn), where the min function returns the minimum f pixel scaling value for all pixels evaluated in the unit from pixel number 1 to n.
 14. The image processing method of claim 9 in which said evaluating comprises: scaling positive or negative adjustments on a per unit basis when luminance values associated with the image prior to encoding fall within a first luminance value range; eliminating positive or negative adjustments on a per unit basis when luminance values associated with the image prior to encoding fall within a second luminance value range, the second luminance value range comprising luminance values above the first luminance value range, and ending at 100% luminance.
 15. The image processing method of claim 9, in which said evaluating alters positive or negative adjustments depending on value ranges of the image prior to receiving encoding, the value ranges comprising: Range i: between 0% luminance to a first predetermined luminance percentage, A %; Range ii: between A % to a second predetermined luminance percentage, C %; and Range iii: between C % to 100% luminance; in which the positive or negative adjustments are not altered when luminance of the image prior to encoding (LU) falls within Range i, the positive or negative adjustments are reduced by a scaling factor when luminance of the image prior to encoding (LU) falls within Range ii, and the positive or negative adjustments are eliminated when luminance of the image prior to encoding (LU) falls within Range iii.
 16. A non-transitory computer readable medium comprising instructions stored thereon, which when executed by one or more multi-core processors, cause the one or more multi-core processors to perform the method of claim
 9. 17. A non-transitory computer readable medium comprising instructions stored thereon, which when executed by one or more multi-core processors, cause the one or more multi-core processors to perform the method of claim
 13. 18. A non-transitory computer readable medium comprising instructions stored thereon, which when executed by one or more multi-core processors, cause the one or more multi-core processors to perform the method of claim
 14. 19. A non-transitory computer readable medium comprising instructions stored thereon, which when executed by one or more multi-core processors, cause the one or more multi-core processors to perform the method of claim
 15. 